GB_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\Bootcamp\\Titanic Spaceship\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import optuna\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc, mean_squared_error\n",
    "from bayes_opt import BayesianOptimization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos el archivo y comprobamos que los datos efectivamente están limpios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0\n",
       "HomePlanet      0\n",
       "CryoSleep       0\n",
       "Cabin           0\n",
       "Destination     0\n",
       "VIP             0\n",
       "Name            0\n",
       "Transported     0\n",
       "Algun_Gasto     0\n",
       "CabinDeck       0\n",
       "CabinSide       0\n",
       "Group           0\n",
       "GroupNumber     0\n",
       "Last Name       0\n",
       "Solo            0\n",
       "Age             0\n",
       "RoomService     0\n",
       "FoodCourt       0\n",
       "ShoppingMall    0\n",
       "Spa             0\n",
       "VRDeck          0\n",
       "Gastos          0\n",
       "CabinNumber     0\n",
       "GroupSize       0\n",
       "AgeGroup        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Train_recoded.csv')\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recoficamos las variables categóricas en numéricas asignandoles valores numéricos en orden (0, 1, 2...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alberto\\AppData\\Local\\Temp\\ipykernel_19144\\2690992866.py:4: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0 0 0 ... 0 0 0]' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n",
      "  df.loc[:, 'CryoSleep'] = df['CryoSleep'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "df.loc[:, 'HomePlanet'] = df['HomePlanet'].map({'Europa': 0, 'Earth': 1, 'Mars': 2}).astype(int)\n",
    "df.loc[:, 'CabinDeck'] = df['CabinDeck'].map({'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'T': 7}).astype(int)\n",
    "df.loc[:, 'CabinSide'] = df['CabinSide'].map({'P': 0, 'S': 1})\n",
    "df.loc[:, 'CryoSleep'] = df['CryoSleep'].astype(int)\n",
    "df.loc[:, 'Destination'] = df['Destination'].map({'TRAPPIST-1e': 0, 'PSO J318.5-22': 1, '55 Cancri e': 2}).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separamos los conjuntos de entrenamiento y test, se eliminan además aquellas columnas que no vayan a usarse para el entrenamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnas_no_usadas = [\"PassengerId\", \"Name\", \"Last Name\", \"Cabin\"]\n",
    "\n",
    "df = df.drop(columns = columnas_no_usadas)\n",
    "\n",
    "y = df['Transported']\n",
    "X = df.drop(['Transported'], axis = 1).astype(int)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3, random_state= 42)\n",
    "\n",
    "eval_set = [(X_train, y_train), (X_test, y_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | learni... | max_depth | min_sa... | min_sa... | n_esti... | subsample |\n",
      "-------------------------------------------------------------------------------------------------\n",
      "| \u001b[39m1        \u001b[39m | \u001b[39m0.7863   \u001b[39m | \u001b[39m0.1186   \u001b[39m | \u001b[39m9.655    \u001b[39m | \u001b[39m14.91    \u001b[39m | \u001b[39m12.78    \u001b[39m | \u001b[39m89.0     \u001b[39m | \u001b[39m0.2404   \u001b[39m |\n",
      "| \u001b[35m2        \u001b[39m | \u001b[35m0.7901   \u001b[39m | \u001b[35m0.02684  \u001b[39m | \u001b[35m9.063    \u001b[39m | \u001b[35m12.42    \u001b[39m | \u001b[35m14.75    \u001b[39m | \u001b[35m55.15    \u001b[39m | \u001b[35m0.9729   \u001b[39m |\n",
      "| \u001b[39m3        \u001b[39m | \u001b[39m0.778    \u001b[39m | \u001b[39m0.2514   \u001b[39m | \u001b[39m4.486    \u001b[39m | \u001b[39m4.455    \u001b[39m | \u001b[39m5.301    \u001b[39m | \u001b[39m126.1    \u001b[39m | \u001b[39m0.5723   \u001b[39m |\n",
      "| \u001b[39m4        \u001b[39m | \u001b[39m0.7809   \u001b[39m | \u001b[39m0.1353   \u001b[39m | \u001b[39m5.039    \u001b[39m | \u001b[39m12.63    \u001b[39m | \u001b[39m4.511    \u001b[39m | \u001b[39m123.0    \u001b[39m | \u001b[39m0.4297   \u001b[39m |\n",
      "| \u001b[39m5        \u001b[39m | \u001b[39m0.763    \u001b[39m | \u001b[39m0.1423   \u001b[39m | \u001b[39m8.496    \u001b[39m | \u001b[39m4.794    \u001b[39m | \u001b[39m11.26    \u001b[39m | \u001b[39m198.1    \u001b[39m | \u001b[39m0.1418   \u001b[39m |\n",
      "| \u001b[39m6        \u001b[39m | \u001b[39m0.7755   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m20.0     \u001b[39m | \u001b[39m2.0      \u001b[39m | \u001b[39m300.0    \u001b[39m | \u001b[39m0.1911   \u001b[39m |\n",
      "| \u001b[35m7        \u001b[39m | \u001b[35m0.8017   \u001b[39m | \u001b[35m0.01942  \u001b[39m | \u001b[35m4.507    \u001b[39m | \u001b[35m2.168    \u001b[39m | \u001b[35m3.579    \u001b[39m | \u001b[35m289.3    \u001b[39m | \u001b[35m0.5095   \u001b[39m |\n",
      "| \u001b[39m8        \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.09859  \u001b[39m | \u001b[39m8.025    \u001b[39m | \u001b[39m13.16    \u001b[39m | \u001b[39m14.28    \u001b[39m | \u001b[39m55.79    \u001b[39m | \u001b[39m0.548    \u001b[39m |\n",
      "| \u001b[39m9        \u001b[39m | \u001b[39m0.793    \u001b[39m | \u001b[39m0.04732  \u001b[39m | \u001b[39m5.399    \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m4.579    \u001b[39m | \u001b[39m282.7    \u001b[39m | \u001b[39m0.647    \u001b[39m |\n",
      "| \u001b[39m10       \u001b[39m | \u001b[39m0.7893   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m8.132    \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m8.995    \u001b[39m | \u001b[39m293.0    \u001b[39m | \u001b[39m0.6583   \u001b[39m |\n",
      "| \u001b[39m11       \u001b[39m | \u001b[39m0.7913   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m8.085    \u001b[39m | \u001b[39m2.0      \u001b[39m | \u001b[39m287.7    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m12       \u001b[39m | \u001b[39m0.7863   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m2.0      \u001b[39m | \u001b[39m295.1    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m13       \u001b[39m | \u001b[39m0.6593   \u001b[39m | \u001b[39m0.245    \u001b[39m | \u001b[39m9.479    \u001b[39m | \u001b[39m2.432    \u001b[39m | \u001b[39m2.0      \u001b[39m | \u001b[39m288.2    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m14       \u001b[39m | \u001b[39m0.7897   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m4.668    \u001b[39m | \u001b[39m3.816    \u001b[39m | \u001b[39m289.2    \u001b[39m | \u001b[39m0.624    \u001b[39m |\n",
      "| \u001b[39m15       \u001b[39m | \u001b[39m0.7872   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m1.062    \u001b[39m | \u001b[39m5.343    \u001b[39m | \u001b[39m291.2    \u001b[39m | \u001b[39m0.7903   \u001b[39m |\n",
      "| \u001b[39m16       \u001b[39m | \u001b[39m0.7872   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m1.153    \u001b[39m | \u001b[39m4.693    \u001b[39m | \u001b[39m286.6    \u001b[39m | \u001b[39m0.7811   \u001b[39m |\n",
      "| \u001b[39m17       \u001b[39m | \u001b[39m0.7868   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m2.0      \u001b[39m | \u001b[39m290.3    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m18       \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.01586  \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m1.043    \u001b[39m | \u001b[39m6.79     \u001b[39m | \u001b[39m279.8    \u001b[39m | \u001b[39m0.9478   \u001b[39m |\n",
      "| \u001b[39m19       \u001b[39m | \u001b[39m0.8013   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.111    \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m9.299    \u001b[39m | \u001b[39m283.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m20       \u001b[39m | \u001b[39m0.7951   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.89     \u001b[39m | \u001b[39m4.605    \u001b[39m | \u001b[39m7.849    \u001b[39m | \u001b[39m282.7    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
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      "| \u001b[39m24       \u001b[39m | \u001b[39m0.7951   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.282    \u001b[39m | \u001b[39m3.178    \u001b[39m | \u001b[39m11.71    \u001b[39m | \u001b[39m286.6    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m25       \u001b[39m | \u001b[39m0.8005   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m11.89    \u001b[39m | \u001b[39m280.2    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m26       \u001b[39m | \u001b[39m0.7751   \u001b[39m | \u001b[39m0.1115   \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m5.897    \u001b[39m | \u001b[39m11.0     \u001b[39m | \u001b[39m279.3    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m27       \u001b[39m | \u001b[39m0.8013   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m7.997    \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m14.29    \u001b[39m | \u001b[39m283.9    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m28       \u001b[39m | \u001b[39m0.5948   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m12.1     \u001b[39m | \u001b[39m287.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m29       \u001b[39m | \u001b[39m0.8022   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m7.034    \u001b[39m | \u001b[39m1.377    \u001b[39m | \u001b[39m12.26    \u001b[39m | \u001b[39m281.7    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m30       \u001b[39m | \u001b[39m0.7988   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.086    \u001b[39m | \u001b[39m3.914    \u001b[39m | \u001b[39m14.33    \u001b[39m | \u001b[39m283.3    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m31       \u001b[39m | \u001b[39m0.8001   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m8.026    \u001b[39m | \u001b[39m1.177    \u001b[39m | \u001b[39m16.13    \u001b[39m | \u001b[39m281.1    \u001b[39m | \u001b[39m0.243    \u001b[39m |\n",
      "| \u001b[39m32       \u001b[39m | \u001b[39m0.7976   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.027    \u001b[39m | \u001b[39m6.237    \u001b[39m | \u001b[39m10.77    \u001b[39m | \u001b[39m283.7    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
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      "| \u001b[39m34       \u001b[39m | \u001b[39m0.793    \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m5.847    \u001b[39m | \u001b[39m9.659    \u001b[39m | \u001b[39m287.2    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m35       \u001b[39m | \u001b[39m0.7972   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m8.131    \u001b[39m | \u001b[39m5.352    \u001b[39m | \u001b[39m7.17     \u001b[39m | \u001b[39m282.5    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m36       \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.955    \u001b[39m | \u001b[39m4.714    \u001b[39m | \u001b[39m4.479    \u001b[39m | \u001b[39m278.7    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
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      "| \u001b[39m39       \u001b[39m | \u001b[39m0.5768   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m8.687    \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m7.957    \u001b[39m | \u001b[39m276.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m40       \u001b[39m | \u001b[39m0.7863   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m5.669    \u001b[39m | \u001b[39m2.378    \u001b[39m | \u001b[39m282.0    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m41       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.462    \u001b[39m | \u001b[39m2.776    \u001b[39m | \u001b[39m7.62     \u001b[39m | \u001b[39m296.9    \u001b[39m | \u001b[39m0.9593   \u001b[39m |\n",
      "| \u001b[39m42       \u001b[39m | \u001b[39m0.7863   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.862    \u001b[39m | \u001b[39m5.141    \u001b[39m | \u001b[39m9.708    \u001b[39m | \u001b[39m292.8    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m43       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m9.864    \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m9.43     \u001b[39m | \u001b[39m298.2    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m44       \u001b[39m | \u001b[39m0.7976   \u001b[39m | \u001b[39m0.1074   \u001b[39m | \u001b[39m9.036    \u001b[39m | \u001b[39m1.745    \u001b[39m | \u001b[39m4.1      \u001b[39m | \u001b[39m298.4    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m45       \u001b[39m | \u001b[39m0.7909   \u001b[39m | \u001b[39m0.1763   \u001b[39m | \u001b[39m9.818    \u001b[39m | \u001b[39m5.548    \u001b[39m | \u001b[39m7.038    \u001b[39m | \u001b[39m297.3    \u001b[39m | \u001b[39m0.5953   \u001b[39m |\n",
      "| \u001b[39m46       \u001b[39m | \u001b[39m0.798    \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.303    \u001b[39m | \u001b[39m5.484    \u001b[39m | \u001b[39m3.795    \u001b[39m | \u001b[39m299.7    \u001b[39m | \u001b[39m0.1496   \u001b[39m |\n",
      "| \u001b[39m47       \u001b[39m | \u001b[39m0.7943   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.08     \u001b[39m | \u001b[39m6.365    \u001b[39m | \u001b[39m4.012    \u001b[39m | \u001b[39m295.2    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m48       \u001b[39m | \u001b[39m0.7697   \u001b[39m | \u001b[39m0.182    \u001b[39m | \u001b[39m4.639    \u001b[39m | \u001b[39m7.979    \u001b[39m | \u001b[39m7.72     \u001b[39m | \u001b[39m298.7    \u001b[39m | \u001b[39m0.1875   \u001b[39m |\n",
      "| \u001b[39m49       \u001b[39m | \u001b[39m0.6639   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m4.652    \u001b[39m | \u001b[39m1.0      \u001b[39m | \u001b[39m4.019    \u001b[39m | \u001b[39m300.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m50       \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m9.358    \u001b[39m | \u001b[39m5.716    \u001b[39m | \u001b[39m2.456    \u001b[39m | \u001b[39m297.7    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m51       \u001b[39m | \u001b[39m0.7947   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.409    \u001b[39m | \u001b[39m6.699    \u001b[39m | \u001b[39m8.474    \u001b[39m | \u001b[39m279.8    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m52       \u001b[39m | \u001b[39m0.798    \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.327    \u001b[39m | \u001b[39m9.143    \u001b[39m | \u001b[39m2.0      \u001b[39m | \u001b[39m299.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[35m53       \u001b[39m | \u001b[35m0.8026   \u001b[39m | \u001b[35m0.01     \u001b[39m | \u001b[35m9.607    \u001b[39m | \u001b[35m8.816    \u001b[39m | \u001b[35m4.645    \u001b[39m | \u001b[35m300.0    \u001b[39m | \u001b[35m0.1      \u001b[39m |\n",
      "| \u001b[39m54       \u001b[39m | \u001b[39m0.5839   \u001b[39m | \u001b[39m0.2154   \u001b[39m | \u001b[39m8.862    \u001b[39m | \u001b[39m10.32    \u001b[39m | \u001b[39m4.127    \u001b[39m | \u001b[39m295.9    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m55       \u001b[39m | \u001b[39m0.8001   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m5.086    \u001b[39m | \u001b[39m4.838    \u001b[39m | \u001b[39m300.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m56       \u001b[39m | \u001b[39m0.7943   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m9.605    \u001b[39m | \u001b[39m3.863    \u001b[39m | \u001b[39m14.29    \u001b[39m | \u001b[39m281.5    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m57       \u001b[39m | \u001b[39m0.7959   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m7.431    \u001b[39m | \u001b[39m3.835    \u001b[39m | \u001b[39m11.58    \u001b[39m | \u001b[39m296.4    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m58       \u001b[39m | \u001b[39m0.7209   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m2.998    \u001b[39m | \u001b[39m11.74    \u001b[39m | \u001b[39m284.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m59       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.746    \u001b[39m | \u001b[39m4.467    \u001b[39m | \u001b[39m7.451    \u001b[39m | \u001b[39m286.0    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m60       \u001b[39m | \u001b[39m0.7951   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m7.508    \u001b[39m | \u001b[39m3.036    \u001b[39m | \u001b[39m17.92    \u001b[39m | \u001b[39m284.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m61       \u001b[39m | \u001b[39m0.7993   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m7.794    \u001b[39m | \u001b[39m7.724    \u001b[39m | \u001b[39m14.76    \u001b[39m | \u001b[39m284.1    \u001b[39m | \u001b[39m0.4204   \u001b[39m |\n",
      "| \u001b[39m62       \u001b[39m | \u001b[39m0.7943   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.311    \u001b[39m | \u001b[39m7.772    \u001b[39m | \u001b[39m11.68    \u001b[39m | \u001b[39m287.6    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m63       \u001b[39m | \u001b[39m0.793    \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.364    \u001b[39m | \u001b[39m6.133    \u001b[39m | \u001b[39m17.03    \u001b[39m | \u001b[39m280.8    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m64       \u001b[39m | \u001b[39m0.7988   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m8.803    \u001b[39m | \u001b[39m9.319    \u001b[39m | \u001b[39m10.33    \u001b[39m | \u001b[39m284.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m65       \u001b[39m | \u001b[39m0.7951   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.263    \u001b[39m | \u001b[39m10.68    \u001b[39m | \u001b[39m12.38    \u001b[39m | \u001b[39m283.7    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m66       \u001b[39m | \u001b[39m0.7997   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.364    \u001b[39m | \u001b[39m8.578    \u001b[39m | \u001b[39m13.11    \u001b[39m | \u001b[39m280.4    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m67       \u001b[39m | \u001b[39m0.7909   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m9.366    \u001b[39m | \u001b[39m11.36    \u001b[39m | \u001b[39m13.98    \u001b[39m | \u001b[39m281.9    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m68       \u001b[39m | \u001b[39m0.6497   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m6.802    \u001b[39m | \u001b[39m11.51    \u001b[39m | \u001b[39m9.377    \u001b[39m | \u001b[39m280.6    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m69       \u001b[39m | \u001b[39m0.7938   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m4.701    \u001b[39m | \u001b[39m9.463    \u001b[39m | \u001b[39m15.85    \u001b[39m | \u001b[39m282.6    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[35m70       \u001b[39m | \u001b[35m0.8047   \u001b[39m | \u001b[35m0.01     \u001b[39m | \u001b[35m9.461    \u001b[39m | \u001b[35m8.431    \u001b[39m | \u001b[35m16.15    \u001b[39m | \u001b[35m280.3    \u001b[39m | \u001b[35m0.1      \u001b[39m |\n",
      "| \u001b[39m71       \u001b[39m | \u001b[39m0.7947   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.635    \u001b[39m | \u001b[39m10.21    \u001b[39m | \u001b[39m16.72    \u001b[39m | \u001b[39m278.5    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m72       \u001b[39m | \u001b[39m0.7959   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m8.576    \u001b[39m | \u001b[39m10.32    \u001b[39m | \u001b[39m18.42    \u001b[39m | \u001b[39m282.7    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m73       \u001b[39m | \u001b[39m0.8017   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m8.666    \u001b[39m | \u001b[39m11.13    \u001b[39m | \u001b[39m13.45    \u001b[39m | \u001b[39m286.5    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m74       \u001b[39m | \u001b[39m0.7901   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m5.144    \u001b[39m | \u001b[39m11.2     \u001b[39m | \u001b[39m15.53    \u001b[39m | \u001b[39m286.9    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m75       \u001b[39m | \u001b[39m0.5935   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m6.396    \u001b[39m | \u001b[39m20.0     \u001b[39m | \u001b[39m281.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m76       \u001b[39m | \u001b[39m0.7955   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.7      \u001b[39m | \u001b[39m6.153    \u001b[39m | \u001b[39m14.4     \u001b[39m | \u001b[39m278.1    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m77       \u001b[39m | \u001b[39m0.8001   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m7.617    \u001b[39m | \u001b[39m13.28    \u001b[39m | \u001b[39m16.75    \u001b[39m | \u001b[39m284.0    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m78       \u001b[39m | \u001b[39m0.7918   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.499    \u001b[39m | \u001b[39m13.32    \u001b[39m | \u001b[39m19.05    \u001b[39m | \u001b[39m281.4    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m79       \u001b[39m | \u001b[39m0.8042   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m9.769    \u001b[39m | \u001b[39m13.27    \u001b[39m | \u001b[39m17.38    \u001b[39m | \u001b[39m280.1    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m80       \u001b[39m | \u001b[39m0.7976   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m14.77    \u001b[39m | \u001b[39m19.83    \u001b[39m | \u001b[39m283.2    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m81       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m11.96    \u001b[39m | \u001b[39m17.74    \u001b[39m | \u001b[39m287.0    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m82       \u001b[39m | \u001b[39m0.7984   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m8.391    \u001b[39m | \u001b[39m17.32    \u001b[39m | \u001b[39m17.1     \u001b[39m | \u001b[39m281.0    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m83       \u001b[39m | \u001b[39m0.7876   \u001b[39m | \u001b[39m0.1915   \u001b[39m | \u001b[39m9.046    \u001b[39m | \u001b[39m15.87    \u001b[39m | \u001b[39m20.0     \u001b[39m | \u001b[39m278.0    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m84       \u001b[39m | \u001b[39m0.7943   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m16.5     \u001b[39m | \u001b[39m15.53    \u001b[39m | \u001b[39m285.3    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m85       \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m6.451    \u001b[39m | \u001b[39m17.38    \u001b[39m | \u001b[39m18.97    \u001b[39m | \u001b[39m285.1    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m86       \u001b[39m | \u001b[39m0.7851   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m10.8     \u001b[39m | \u001b[39m9.916    \u001b[39m | \u001b[39m287.7    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m87       \u001b[39m | \u001b[39m0.7997   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m7.669    \u001b[39m | \u001b[39m15.03    \u001b[39m | \u001b[39m14.83    \u001b[39m | \u001b[39m289.1    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m88       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.125    \u001b[39m | \u001b[39m16.24    \u001b[39m | \u001b[39m14.15    \u001b[39m | \u001b[39m285.7    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m89       \u001b[39m | \u001b[39m0.7984   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m14.52    \u001b[39m | \u001b[39m11.31    \u001b[39m | \u001b[39m287.9    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m90       \u001b[39m | \u001b[39m0.7993   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m10.37    \u001b[39m | \u001b[39m15.03    \u001b[39m | \u001b[39m277.1    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m91       \u001b[39m | \u001b[39m0.7876   \u001b[39m | \u001b[39m0.1738   \u001b[39m | \u001b[39m8.329    \u001b[39m | \u001b[39m18.95    \u001b[39m | \u001b[39m12.51    \u001b[39m | \u001b[39m288.6    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m92       \u001b[39m | \u001b[39m0.7872   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m9.776    \u001b[39m | \u001b[39m16.62    \u001b[39m | \u001b[39m18.85    \u001b[39m | \u001b[39m289.2    \u001b[39m | \u001b[39m0.8784   \u001b[39m |\n",
      "| \u001b[39m93       \u001b[39m | \u001b[39m0.7951   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m11.74    \u001b[39m | \u001b[39m13.91    \u001b[39m | \u001b[39m290.9    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m94       \u001b[39m | \u001b[39m0.7918   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m16.23    \u001b[39m | \u001b[39m13.58    \u001b[39m | \u001b[39m292.7    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m95       \u001b[39m | \u001b[39m0.7868   \u001b[39m | \u001b[39m0.3      \u001b[39m | \u001b[39m6.266    \u001b[39m | \u001b[39m13.59    \u001b[39m | \u001b[39m11.39    \u001b[39m | \u001b[39m290.1    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m96       \u001b[39m | \u001b[39m0.7938   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m6.953    \u001b[39m | \u001b[39m12.81    \u001b[39m | \u001b[39m17.52    \u001b[39m | \u001b[39m292.3    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m97       \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.876    \u001b[39m | \u001b[39m16.04    \u001b[39m | \u001b[39m17.68    \u001b[39m | \u001b[39m289.6    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m98       \u001b[39m | \u001b[39m0.7943   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.392    \u001b[39m | \u001b[39m17.26    \u001b[39m | \u001b[39m15.29    \u001b[39m | \u001b[39m293.4    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m99       \u001b[39m | \u001b[39m0.7926   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m10.1     \u001b[39m | \u001b[39m9.626    \u001b[39m | \u001b[39m287.6    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m100      \u001b[39m | \u001b[39m0.793    \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.967    \u001b[39m | \u001b[39m8.276    \u001b[39m | \u001b[39m14.99    \u001b[39m | \u001b[39m292.4    \u001b[39m | \u001b[39m0.9422   \u001b[39m |\n",
      "| \u001b[39m101      \u001b[39m | \u001b[39m0.798    \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m15.57    \u001b[39m | \u001b[39m15.39    \u001b[39m | \u001b[39m276.6    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m102      \u001b[39m | \u001b[39m0.7943   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.948    \u001b[39m | \u001b[39m12.91    \u001b[39m | \u001b[39m20.0     \u001b[39m | \u001b[39m286.9    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "| \u001b[39m103      \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m3.0      \u001b[39m | \u001b[39m12.71    \u001b[39m | \u001b[39m14.93    \u001b[39m | \u001b[39m293.1    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m104      \u001b[39m | \u001b[39m0.8017   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m7.394    \u001b[39m | \u001b[39m12.91    \u001b[39m | \u001b[39m14.56    \u001b[39m | \u001b[39m296.1    \u001b[39m | \u001b[39m0.1      \u001b[39m |\n",
      "| \u001b[39m105      \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m9.753    \u001b[39m | \u001b[39m9.514    \u001b[39m | \u001b[39m16.0     \u001b[39m | \u001b[39m295.6    \u001b[39m | \u001b[39m1.0      \u001b[39m |\n",
      "=================================================================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.01, max_depth=9, min_samples_leaf=8,\n",
       "                           min_samples_split=16, n_estimators=280,\n",
       "                           random_state=42, subsample=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(learning_rate=0.01, max_depth=9, min_samples_leaf=8,\n",
       "                           min_samples_split=16, n_estimators=280,\n",
       "                           random_state=42, subsample=0.1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.01, max_depth=9, min_samples_leaf=8,\n",
       "                           min_samples_split=16, n_estimators=280,\n",
       "                           random_state=42, subsample=0.1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def gbc_evaluate(n_estimators, max_depth, min_samples_split, min_samples_leaf, subsample, learning_rate):\n",
    "    model = GradientBoostingClassifier(\n",
    "        n_estimators=int(n_estimators),\n",
    "        max_depth=int(max_depth),\n",
    "        min_samples_split=int(min_samples_split),\n",
    "        min_samples_leaf=int(min_samples_leaf),\n",
    "        subsample=subsample,\n",
    "        learning_rate=learning_rate,\n",
    "        random_state=42\n",
    "    )\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    return accuracy  # Queremos minimizar el MSE\n",
    "\n",
    "# Definir los límites de los hiperparámetros a optimizar\n",
    "pbounds = {\n",
    "    'n_estimators': (50, 300),\n",
    "    'max_depth': (3, 10),\n",
    "    'min_samples_split': (2, 20),\n",
    "    'min_samples_leaf': (1, 20),\n",
    "    'subsample': (0.1, 1),\n",
    "    'learning_rate': (0.01, 0.3),\n",
    "}\n",
    "\n",
    "# Optimización Bayesiana\n",
    "optimizer = BayesianOptimization(\n",
    "    f=gbc_evaluate,\n",
    "    pbounds=pbounds,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "# Aumentar el número de iteraciones para maximizar la búsqueda\n",
    "optimizer.maximize(init_points=5, n_iter=100)\n",
    "\n",
    "# Una vez que la búsqueda ha terminado, obtenemos los mejores parámetros\n",
    "best_params = optimizer.max['params']\n",
    "best_params['n_estimators'] = int(best_params['n_estimators'])\n",
    "best_params['max_depth'] = int(best_params['max_depth'])\n",
    "best_params['min_samples_split'] = int(best_params['min_samples_split'])\n",
    "best_params['min_samples_leaf'] = int(best_params['min_samples_leaf'])\n",
    "\n",
    "# Entrenar el modelo final con los mejores parámetros\n",
    "final_model = GradientBoostingClassifier(\n",
    "    **best_params,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "final_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sacamos los resultados de accuracy, validación cruzada y pintamos los gráficos de curva roc y matriz de confusión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Puntajes de validación cruzada: [0.68019988 0.734375   0.788125   0.81375    0.783125  ]\n",
      "Media de los puntajes: 0.7599149750156153\n"
     ]
    }
   ],
   "source": [
    "y_pred = final_model.predict(X_test)\n",
    "\n",
    "# Validación cruzada\n",
    "cv_scores = cross_val_score(final_model, X, y, cv=5)\n",
    "\n",
    "print(f\"Puntajes de validación cruzada: {cv_scores}\")\n",
    "print(f\"Media de los puntajes: {cv_scores.mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del modelo: 0.8046647230320699\n",
      "Reporte de clasificación:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.81      0.79      0.80      1183\n",
      "        True       0.80      0.82      0.81      1218\n",
      "\n",
      "    accuracy                           0.80      2401\n",
      "   macro avg       0.80      0.80      0.80      2401\n",
      "weighted avg       0.80      0.80      0.80      2401\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "\n",
    "# Visualizar la matriz de confusión\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Clase 0\", \"Clase 1\"], yticklabels=[\"Clase 0\", \"Clase 1\"])\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "print(f\"Precisión del modelo: {accuracy}\")\n",
    "print(\"Reporte de clasificación:\\n\", class_report)\n",
    "\n",
    "# Calcular y graficar la curva ROC\n",
    "y_prob = final_model.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos hacer también un análisis de las features que hemos incluido en el modelo para comprobar si puede suprimirse alguna que no aporte al modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yNX2IXC5XGJXU0tKCi4tLgd+bd6Wnp4vnv2v//v2oUKGCuFWrVk3p3G7duqFChQoKZerq6uJ9zMvLw/Pnz5GTkwNnZ2eF+75//35oaGhg2LBhCueOHDnyo9da3M99Se/Nu7S1tcV/Kw4dOoRVq1ZBLpejbdu2Covc7N+/Hy4uLgpTSOVyOQYPHozk5GRx6uT+/fthYmKCXr16ifU0NTUxatQoZGRk4Pjx4wDefk5evXr10SnVxfWxz/uFCxfw7NkzDBo0SGFqsI+PDwwNDRXaSk9Ph66u7ifF8+6/py9evEBaWhqaNGmi8JnJ/7nbtWuXwjTVdxkYGODhw4c4f/78J8VTmG3btqFJkyYwNDRU+Oy1bNkSubm5OHHihEL9gn5GiMoKTp0lojLJxcWlwMWA8n/pb968eYHn6enpiV8/f/4cwcHB2Lx5M54+fapQLy0tTYXR/p/3p6cmJiZCEARYW1sXWP/dRK84tLW1lX450tfXR5UqVZSeldPX1y/w2cv3Y5LL5TA1NRWfi7p37x6At8nqu7S0tGBpaSkez2dmZlasRTTy8vKwZMkSrFixAklJScjNzRWPGRsbK9WvWrWqwn7+L9P513bnzh0AH16d+Pbt2xAEAVOnTsXUqVMLrPP06VOYmZlhxowZ6NSpE2xsbFC7dm14eXmhT58+cHR0LPI1vqug742hoSGuXLnywfPyE4SMjAyFcjc3NzG5mT9/vsL0xHzvfx7zRUREYOHChbh58yays7MLrH/v3j2YmpoqJbjvfx4KUtzPfUnvzbvU1dXRsmVLhbK2bdvC2toagYGBiIyMBPD2uho2bKh0fv5U1nv37qF27dq4d+8erK2tlVY0fbceAPj7+2Pr1q1o06YNzMzM0Lp1a3h7e8PLy6vIsRfkY5/3/P6trKwU6mloaCitbKunp1espL0ge/fuxcyZMxEbG6vwKp13v289evTA2rVrMXDgQEyaNAktWrRA165d8e2334r3ceLEifjjjz/g4uICKysrtG7dGr1794abm9snxZcvMTERV65cKTR5fP//BYX9jBCVBUw0iYjekf9X8l9++UVc6ONd7/5l39vbG6dPn8b48eNRp04dyOVy5OXlwcvLq9C/tr+rsBfQv5sQve/dv/rnxyuRSHDgwAGlVRQB5VGqoiqorQ+VC///edHP6f1r/5jZs2dj6tSpGDBgAH788UcYGRlBTU0NAQEBBX5/VHFt+e2OGzcOnp6eBdbJ/8W9adOmuHPnDnbt2oXDhw9j7dq1+Pnnn7Fy5UqF0eSiKmn8dnZ2AIBr167ByclJLK9QoYKYWP36668FnlvQ9+TXX3+Fr68vOnfujPHjx6NixYpQV1fHnDlzxGT9UxX3c/+5PrdVqlSBra2t0iiWKlWsWBGxsbE4dOgQDhw4gAMHDmD9+vXo27dvgQsHFZUq74mdnR0uX76MBw8ewNzcvNjnnzx5Eh07dkTTpk2xYsUKmJqaQlNTE+vXr1dYxEdHRwcnTpzAsWPHsG/fPhw8eBBbtmxB8+bNcfjwYairq8Pe3h4JCQnYu3cvDh48iMjISKxYsQLTpk1DcHBwsWN7X15eHlq1aoUJEyYUeNzGxkZhv7j/bhH9lzDRJCJ6R40aNQC8/eXu/dGLd7148QJHjx5FcHAwpk2bJpYXNA2ysIQyfwTh/VU23x/J+1i8giCgevXqSr/glLbExER4eHiI+xkZGUhJSUHbtm0BQJyKmZCQAEtLS7HemzdvkJSU9MH7/67C7u/27dvh4eGh9O7H1NRUcVGm4sj/bFy7dq3Q2PKvQ1NTs0jxGxkZoX///ujfvz8yMjLQtGlTBAUFlSjRLKk2bdpAXV0dGzduhI+Pzye3t337dlhaWmLHjh0K35vp06cr1KtWrRqOHj2KjIwMhcQwISHho318js99YZ+jj8nJyVEYDa5WrVqB13Dz5k3xeP5/r1y5gry8PIVRzffrAW9H+Tt06IAOHTogLy8P/v7+WLVqFaZOnQorK6sSx/4h+f3fvn1b4ec4JycHycnJCiPvHTp0wKZNm/Drr78iMDCw2H1FRkZCW1sbhw4dUnidyPr165XqqqmpoUWLFmjRogUWLVqE2bNnY/LkyTh27Jj4MyeTydCjRw/06NEDb968QdeuXTFr1iwEBgZ+8quBatSogYyMjCL/+0RUlvEZTSKid3h6ekJPTw+zZ89WmPKXL3+l2PzRgPf/+v/+ioMAxHddvp9Q6unpoXz58kqjIStWrChyvF27doW6ujqCg4OVYhEEQeFVK/+21atXK9zD0NBQ5OTkiCugtmzZElpaWli6dKlC7GFhYUhLSxNXmvwYmUxW4Csx1NXVle7Jtm3bxGcki6tevXqoXr06Fi9erNRffj8VK1aEu7s7Vq1ahZSUFKU23l1p+P3vjVwuh5WVlcK0wX9D1apVMWDAABw4cADLly8vsE5xRrkK+tk4d+4czpw5o1Cvbdu2yMnJQWhoqFiWm5uLZcuWfbSPz/G5L+zn9ENu3bqFhIQEhZHgtm3bIiYmRuF6X716hdWrV8PCwgI1a9YU6z1+/BhbtmwR6+Xk5GDZsmWQy+Vo1qwZAOXPiZqampjk5X9WShL7xzg7O8PY2Bhr1qxBTk6OWL5x40alqfLffvstHBwcMGvWLKXvMwC8fPlSXBW6IOrq6pBIJAqzOZKTk/H7778r1Hv+/LnSufmrGOffi/fvl5aWFmrWrAlBEAr8N724vL29cebMGRw6dEjpWGpqqsK9IirrOKJJRPQOPT09hIaGok+fPqhXrx569uyJChUq4P79+9i3bx/c3NywfPly6Onpia/+yM7OhpmZGQ4fPoykpCSlNuvXrw8AmDx5Mnr27AlNTU106NABMpkMAwcOxE8//YSBAwfC2dkZJ06cUFhY5GNq1KiBmTNnIjAwUHz1gK6uLpKSkrBz504MHjwY48aNU9n9KY43b96gRYsW8Pb2RkJCAlasWIFvvvkGHTt2BPB2amZgYCCCg4Ph5eWFjh07ivUaNGhQ5Ndt1K9fH6GhoZg5cyasrKxQsWJFNG/eHO3bt8eMGTPQv39/NG7cGFevXsXGjRsVRk+LQ01NDaGhoejQoQPq1KmD/v37w9TUFDdv3sT169fFXzxDQkLwzTffwMHBAYMGDYKlpSWePHmCM2fO4OHDh+J7PGvWrAl3d3fUr18fRkZGuHDhArZv344RI0aUKL5PsXjxYiQlJWHkyJHYvHkzOnTogIoVK+Lvv/9GdHQ09uzZU6RnJwGgffv22LFjB7p06YJ27dohKSkJK1euRM2aNRVG/jp06AA3NzdMmjQJycnJqFmzJnbs2FGk55s/x+e+Ro0aMDAwwMqVK6GrqwuZTIaGDRuKz9jl5OSIU4jz8vKQnJyMlStXIi8vT2G0dtKkSdi0aRPatGmDUaNGwcjICBEREUhKSkJkZKQ4ejl48GCsWrUKvr6+uHjxIiwsLLB9+3ZER0dj8eLF4rOzAwcOxPPnz9G8eXNUqVIF9+7dw7Jly1CnTh3xec46depAXV0dc+fORVpaGqRSKZo3b46KFSsW6x68S0tLC0FBQRg5ciSaN28Ob29vJCcnIzw8HDVq1FAYRdXU1MSOHTvQsmVLNG3aFN7e3nBzc4OmpiauX7+O3377DYaGhoW+S7Ndu3ZYtGgRvLy80Lt3bzx9+hQhISGwsrJSeI52xowZOHHiBNq1a4dq1arh6dOnWLFiBapUqSIuvtS6dWuYmJjAzc0NlSpVQnx8PJYvX4527dp98oJFADB+/Hjs3r0b7du3h6+vL+rXr49Xr17h6tWr2L59O5KTk0s0Y4LoP+lfXeOWiKiUFfQ6j4IcO3ZM8PT0FPT19QVtbW2hRo0agq+vr3DhwgWxzsOHD4UuXboIBgYGgr6+vtC9e3fh0aNHSq/7EIS3S+KbmZkJampqCsvqZ2ZmCn5+foK+vr6gq6sreHt7C0+fPi309SaFLesfGRkpfPPNN4JMJhNkMplgZ2cnDB8+XEhISCj2/Shsmf9mzZoV+NqAatWqKbymI7/N48ePC4MHDxYMDQ0FuVwu+Pj4CM+ePVM6f/ny5YKdnZ2gqakpVKpUSRg2bJjS60MK61sQ3r56pl27doKurq7Caxdev34tjB07VjA1NRV0dHQENzc34cyZM4W+7mHbtm0K7Rb2+plTp04JrVq1EnR1dQWZTCY4OjoKy5YtU6hz584doW/fvoKJiYmgqakpmJmZCe3btxe2b98u1pk5c6bg4uIiGBgYCDo6OoKdnZ0wa9YshVfCFKSw15sUdH8Keo1EYXJycoT169cLzZs3F4yMjAQNDQ2hfPnyQosWLYSVK1cK//zzj9K9mT9/vlI7eXl5wuzZs4Vq1aoJUqlUqFu3rrB3794CY3n27JnQp08fQU9PT9DX1xf69OkjXL58+aOvN8lXlM99ce7Nrl27hJo1awoaGhoKMRT0ehM9PT2hRYsWwh9//KHU9p07d4Rvv/1WMDAwELS1tQUXFxdh7969SvWePHki9O/fXyhfvrygpaUlODg4KH3etm/fLrRu3VqoWLGioKWlJVStWlUYMmSIkJKSolBvzZo1gqWlpaCurq7w+fjUz/vSpUvF76WLi4sQHR0t1K9fX/Dy8lK6nhcvXgjTpk0THBwchHLlygna2tpC7dq1hcDAQIV4C7r3YWFhgrW1tSCVSgU7Ozth/fr1St/3o0ePCp06dRIqV64saGlpCZUrVxZ69eql8KqRVatWCU2bNhWMjY0FqVQq1KhRQxg/fryQlpYm1vmU15sIwtvX1QQGBgpWVlaClpaWUL58eaFx48bCggULxJ/fD/2MEJUVEkH4F1ZwICKiMiM8PBz9+/fH+fPnC1zZl4i+Xnl5eahQoQK6du2KNWvWlHY4RPQF4zOaRERERKTk9evXSs/AbtiwAc+fP4e7u3vpBEVEXw0+o0lERERESs6ePYvvv/8e3bt3h7GxMS5duoSwsDDUrl0b3bt3L+3wiOgLx0STiIiIiJRYWFjA3NwcS5cuxfPnz2FkZIS+ffvip59+gpaWVmmHR0RfOD6jSURERERERCrFZzSJiIiIiIhIpZhoEhERERERkUrxGU36oLy8PDx69Ai6uroKL2cmIiIiIqKyRRAEvHz5EpUrV4aa2ofHLJlo0gc9evQI5ubmpR0GERERERF9IR48eIAqVap8sA4TTfogXV1dAG8/THp6eqUcDRERERERlZb09HSYm5uLOcKHMNGkD8qfLqunp8dEk4iIiIiIivRIHRcDIiIiIiIiIpVioklEREREREQqxUSTiIiIiIiIVIqJJhEREREREakUE00iIiIiIiJSKSaaREREREREpFJMNImIiIiIiEilmGgSERERERGRSjHRJCIiIiIiIpVioklEREREREQqxUSTiIiIiIiIVIqJJhEREREREakUE00iIiIiIiJSKSaaREREREREpFJMNImIiIiIiEilmGgSERERERGRSjHRJCIiIiIiIpVioklEREREREQqpVHaAdDXYVHcM2jL35R2GEREREREZcakuuVLO4QS44gmERERERERqRQTTSIiIiIiIlIpJppERERERESkUkw0Vejx48cYPXo0rKysoK2tjUqVKsHNzQ2hoaHIzMz85PajoqIgkUiQmpr66cESERERERF9JlwMSEXu3r0LNzc3GBgYYPbs2XBwcIBUKsXVq1exevVqmJmZoWPHjqUdJhERERER0WfHEU0V8ff3h4aGBi5cuABvb2/Y29vD0tISnTp1wr59+9ChQwcAwKJFi+Dg4ACZTAZzc3P4+/sjIyNDbOfevXvo0KEDDA0NIZPJUKtWLezfvx/Jycnw8PAAABgaGkIikcDX1xcAkJWVhVGjRqFixYrQ1tbGN998g/Pnz4ttvnjxAj4+PqhQoQJ0dHRgbW2N9evX/3s3h4iIiIiIyhSOaKrAs2fPcPjwYcyePRsymazAOhKJBACgpqaGpUuXonr16rh79y78/f0xYcIErFixAgAwfPhwvHnzBidOnIBMJsONGzcgl8thbm6OyMhIdOvWDQkJCdDT04OOjg4AYMKECYiMjERERASqVauGefPmwdPTE7dv34aRkRGmTp2KGzdu4MCBAyhfvjxu376Nf/75p8A4s7KykJWVJe6np6er8lYREREREVEZwERTBW7fvg1BEGBra6tQXr58ebx+/RrA2wRy7ty5CAgIEI9bWFhg5syZGDp0qJho3r9/H926dYODgwMAwNLSUqxvZGQEAKhYsSIMDAwAAK9evUJoaCjCw8PRpk0bAMCaNWtw5MgRhIWFYfz48bh//z7q1q0LZ2dnsd/CzJkzB8HBwSW/GUREREREVOZx6uxnFBMTg9jYWNSqVUscJfzjjz/QokULmJmZQVdXF3369MGzZ8/ExYJGjRqFmTNnws3NDdOnT8eVK1c+2MedO3eQnZ0NNzc3sUxTUxMuLi6Ij48HAAwbNgybN29GnTp1MGHCBJw+fbrQ9gIDA5GWliZuDx48+NTbQEREREREZQwTTRWwsrKCRCJBQkKCQrmlpSWsrKzEKa7Jyclo3749HB0dERkZiYsXLyIkJAQA8ObNGwDAwIEDcffuXfTp0wdXr16Fs7Mzli1b9knxtWnTBvfu3cP333+PR48eoUWLFhg3blyBdaVSKfT09BQ2IiIiIiKi4mCiqQLGxsZo1aoVli9fjlevXhVa7+LFi8jLy8PChQvRqFEj2NjY4NGjR0r1zM3NMXToUOzYsQNjx47FmjVrAABaWloAgNzcXLFujRo1oKWlhejoaLEsOzsb58+fR82aNcWyChUqoF+/fvj111+xePFirF69+pOvm4iIiIiIqCBMNFVkxYoVyMnJgbOzM7Zs2YL4+HgkJCTg119/xc2bN6Gurg4rKytkZ2dj2bJluHv3Ln755ResXLlSoZ2AgAAcOnQISUlJuHTpEo4dOwZ7e3sAQLVq1SCRSLB3717873//Q0ZGBmQyGYYNG4bx48fj4MGDuHHjBgYNGoTMzEz4+fkBAKZNm4Zdu3bh9u3buH79Ovbu3Su2SUREREREpGpMNFWkRo0auHz5Mlq2bInAwEA4OTmJ017HjRuHH3/8EU5OTli0aBHmzp2L2rVrY+PGjZgzZ45CO7m5uRg+fDjs7e3h5eUFGxsbcaEgMzMzBAcHY9KkSahUqRJGjBgBAPjpp5/QrVs39OnTB/Xq1cPt27dx6NAhGBoaAng7EhoYGAhHR0c0bdoU6urq2Lx58797g4iIiIiIqMyQCIIglHYQ9OVKT0+Hvr4+pp+4C225bmmHQ0RERERUZkyqW760Q1CQnxukpaV9dC0XjmgSERERERGRSjHRJCIiIiIiIpXSKO0A6OswxsmYrzohIiIiIqIi4YgmERERERERqRQTTSIiIiIiIlIpJppERERERESkUnxGk4pkUdwzaMvflHYYREREVMq+tNctENGXiSOaREREREREpFJMNImIiIiIiEilmGgSERERERGRSv2nEs3w8HAYGBh8sE5QUBDq1Knzr8SjKlFRUZBIJEhNTS3tUIiIiIiIiD7qi0o0Hz9+jJEjR8LS0hJSqRTm5ubo0KEDjh49qrI+xo0bV+z23N3dIZFIsHnzZoXyxYsXw8LCQmWxERERERER/Rd8MYlmcnIy6tevjz///BPz58/H1atXcfDgQXh4eGD48OEq60cul8PY2LjY52lra2PKlCnIzs5WWSyl7c0briJLRERERESq98Ukmv7+/pBIJIiJiUG3bt1gY2ODWrVqYcyYMTh79iwAYNGiRXBwcIBMJoO5uTn8/f2RkZGh1Nbvv/8Oa2traGtrw9PTEw8ePBCPvT911tfXF507d8aCBQtgamoKY2NjDB8+XCmh7NWrF1JTU7FmzZpCryG/rXcFBATA3d1d3Hd3d8fIkSMREBAAQ0NDVKpUCWvWrMGrV6/Qv39/6OrqwsrKCgcOHFBqPzo6Go6OjtDW1kajRo1w7do1heOnTp1CkyZNoKOjA3Nzc4waNQqvXr0Sj1tYWODHH39E3759oaenh8GDByv1kZWVhfT0dIWNiIiIiIioOL6IRPP58+c4ePAghg8fDplMpnQ8/7lLNTU1LF26FNevX0dERAT+/PNPTJgwQaFuZmYmZs2ahQ0bNiA6Ohqpqano2bPnB/s/duwY7ty5g2PHjiEiIgLh4eEIDw9XqKOnp4fJkydjxowZCslbSURERKB8+fKIiYnByJEjMWzYMHTv3h2NGzfGpUuX0Lp1a/Tp0weZmZkK540fPx4LFy7E+fPnUaFCBXTo0EFMiO/cuQMvLy9069YNV65cwZYtW3Dq1CmMGDFCoY0FCxbAyckJly9fxtSpU5VimzNnDvT19cXN3Nz8k66ViIiIiIjKni8i0bx9+zYEQYCdnd0H6wUEBMDDwwMWFhZo3rw5Zs6cia1btyrUyc7OxvLly+Hq6or69esjIiICp0+fRkxMTKHtGhoaYvny5bCzs0P79u3Rrl27Ap/j9Pf3h7a2NhYtWlSyC/3/nJycMGXKFFhbWyMwMBDa2tooX748Bg0aBGtra0ybNg3Pnj3DlStXFM6bPn06WrVqBQcHB0RERODJkyfYuXMngLcJoo+PDwICAmBtbY3GjRtj6dKl2LBhA16/fi220bx5c4wdOxY1atRAjRo1lGILDAxEWlqauL07GkxERERERFQUX0SiKQhCker98ccfaNGiBczMzKCrq4s+ffrg2bNnCiN/GhoaaNCggbhvZ2cHAwMDxMfHF9purVq1oK6uLu6bmpri6dOnSvWkUilmzJiBBQsW4O+//y5SzAVxdHQUv1ZXV4exsTEcHBzEskqVKgGAUgyurq7i10ZGRrC1tRWvKy4uDuHh4ZDL5eLm6emJvLw8JCUliec5Ozt/MDapVAo9PT2FjYiIiIiIqDi+iETT2toaEokEN2/eLLROcnIy2rdvD0dHR0RGRuLixYsICQkB8OmL2mhqairsSyQS5OXlFVj3u+++Q7Vq1TBz5kylY2pqakpJc0GLBxXU37tlEokEAAqNoSAZGRkYMmQIYmNjxS0uLg6JiYkKI5cFTU0mIiIiIiJSpS8i0TQyMoKnpydCQkIKfP4xNTUVFy9eRF5eHhYuXIhGjRrBxsYGjx49Uqqbk5ODCxcuiPsJCQlITU2Fvb29SmJVU1PDnDlzEBoaiuTkZIVjFSpUQEpKikJZbGysSvoFIC6KBAAvXrzArVu3xOuqV68ebty4ASsrK6VNS0tLZTEQERERERF9zBeRaAJASEgIcnNz4eLigsjISCQmJiI+Ph5Lly6Fq6srrKyskJ2djWXLluHu3bv45ZdfsHLlSqV2NDU1MXLkSJw7dw4XL16Er68vGjVqBBcXF5XF2q5dOzRs2BCrVq1SKG/evDkuXLiADRs2IDExEdOnT1daGfZTzJgxA0ePHsW1a9fg6+uL8uXLi6vcTpw4EadPn8aIESMQGxuLxMRE7Nq1S2kxICIiIiIios/ti0k0LS0tcenSJXh4eGDs2LGoXbs2WrVqhaNHjyI0NBROTk5YtGgR5s6di9q1a2Pjxo2YM2eOUjvlypXDxIkT0bt3b7i5uUEul2PLli0qj3fu3LkKi+wAgKenJ6ZOnYoJEyagQYMGePnyJfr27auyPn/66SeMHj0a9evXx+PHj7Fnzx5xtNLR0RHHjx/HrVu30KRJE9StWxfTpk1D5cqVVdY/ERERERFRUUiEoq7EQ2VSeno69PX1Mf3EXWjLdUs7HCIiIiplk+qWL+0QiKiU5OcGaWlpH1009IsZ0SQiIiIiIqL/Bo3SDoC+DmOcjPmqEyIiIiIiKhKOaBIREREREZFKMdEkIiIiIiIilWKiSURERERERCrFZzSpSBbFPYO2/E1ph0FERPRV44qtRFRWcESTiIiIiIiIVIqJJhEREREREakUE00iIiIiIiJSKSaaREREREREpFJMND+zx48fY/To0bCysoK2tjYqVaoENzc3hIaGIjMzs7TDIyIiIiIiUjmuOvsZ3b17F25ubjAwMMDs2bPh4OAAqVSKq1evYvXq1TAzM0PHjh2VzsvOzoampmYpRExERERERPTpOKL5Gfn7+0NDQwMXLlyAt7c37O3tYWlpiU6dOmHfvn3o0KEDAEAikSA0NBQdO3aETCbDrFmzAAChoaGoUaMGtLS0YGtri19++UVsOzk5GRKJBLGxsWJZamoqJBIJoqKiAABRUVGQSCTYt28fHB0doa2tjUaNGuHatWv/2j0gIiIiIqKyh4nmZ/Ls2TMcPnwYw4cPh0wmK7CORCIRvw4KCkKXLl1w9epVDBgwADt37sTo0aMxduxYXLt2DUOGDEH//v1x7NixYscyfvx4LFy4EOfPn0eFChXQoUMHZGdnF1g3KysL6enpChsREREREVFxMNH8TG7fvg1BEGBra6tQXr58ecjlcsjlckycOFEs7927N/r37w9LS0tUrVoVCxYsgK+vL/z9/WFjY4MxY8aga9euWLBgQbFjmT59Olq1agUHBwdERETgyZMn2LlzZ4F158yZA319fXEzNzcvdn9ERERERFS2MdH8l8XExCA2Nha1atVCVlaWWO7s7KxQLz4+Hm5ubgplbm5uiI+PL3afrq6u4tdGRkawtbUttJ3AwECkpaWJ24MHD4rdHxERERERlW1cDOgzsbKygkQiQUJCgkK5paUlAEBHR0ehvLDptYVRU3v7NwJBEMSywqbDFodUKoVUKv3kdoiIiIiIqOziiOZnYmxsjFatWmH58uV49epVsc+3t7dHdHS0Qll0dDRq1qwJAKhQoQIAICUlRTz+7sJA7zp79qz49YsXL3Dr1i3Y29sXOyYiIiIiIqKi4IjmZ7RixQq4ubnB2dkZQUFBcHR0hJqaGs6fP4+bN2+ifv36hZ47fvx4eHt7o27dumjZsiX27NmDHTt24I8//gDwdkS0UaNG+Omnn1C9enU8ffoUU6ZMKbCtGTNmwNjYGJUqVcLkyZNRvnx5dO7c+XNcMhERERERERPNz6lGjRq4fPkyZs+ejcDAQDx8+BBSqRQ1a9bEuHHj4O/vX+i5nTt3xpIlS7BgwQKMHj0a1atXx/r16+Hu7i7WWbduHfz8/FC/fn3Y2tpi3rx5aN26tVJbP/30E0aPHo3ExETUqVMHe/bsgZaW1ue4ZCIiIiIiIkiEdx/yo/+UqKgoeHh44MWLFzAwMChRG+np6dDX18f0E3ehLddVbYBERERlzKS65Us7BCKiEsvPDdLS0qCnp/fBunxGk4iIiIiIiFSKU2epSMY4GX/0rxZEREREREQAE83/NHd3d3BmNBERERER/ds4dZaIiIiIiIhUiokmERERERERqRSnzlKRLIp7Bm35m9IOg4iICABXbyUi+tJxRJOIiIiIiIhUiokmERERERERqRQTTSIiIiIiIlIpJpqfQVRUFCQSCVJTU0s7FCIiIiIion9dmUg0fX19IZFIlLbbt2//azEIgoDVq1ejYcOGkMvlMDAwgLOzMxYvXozMzMzP3r+vry86d+782fshIiIiIiIqE4kmAHh5eSElJUVhq169+r/Wf58+fRAQEIBOnTrh2LFjiI2NxdSpU7Fr1y4cPnz4s/Wbm5uLvLy8z9Y+ERERERHR+8pMoimVSmFiYqKwqaur4/jx43BxcYFUKoWpqSkmTZqEnJwc8bysrCyMGjUKFStWhLa2Nr755hucP39eoe39+/fDxsYGOjo68PDwQHJyssLxrVu3YuPGjdi0aRN++OEHNGjQABYWFujUqRP+/PNPeHh4AADy8vIwY8YMVKlSBVKpFHXq1MHBgwfFdgqakhsbGwuJRCL2GR4eDgMDA+zevRs1a9aEVCrFgAEDEBERgV27domjuVFRUSq9v0RERERERPnK9Hs0//rrL7Rt2xa+vr7YsGEDbt68iUGDBkFbWxtBQUEAgAkTJiAyMhIRERGoVq0a5s2bB09PT9y+fRtGRkZ48OABunbtiuHDh2Pw4MG4cOECxo4dq9DPxo0bYWtri06dOinFIJFIoK+vDwBYsmQJFi5ciFWrVqFu3bpYt24dOnbsiOvXr8Pa2rrI15WZmYm5c+di7dq1MDY2hqmpKf755x+kp6dj/fr1AAAjI6MCz83KykJWVpa4n56eXuR+iYiIiIiIgDKUaO7duxdyuVzcb9OmDWxsbGBubo7ly5dDIpHAzs4Ojx49wsSJEzFt2jT8888/CA0NRXh4ONq0aQMAWLNmDY4cOYKwsDCMHz8eoaGhqFGjBhYuXAgAsLW1xdWrVzF37lyxr8TERNja2n40xgULFmDixIno2bMnAGDu3Lk4duwYFi9ejJCQkCJfa3Z2NlasWAEnJyexTEdHB1lZWTAxMfnguXPmzEFwcHCR+yIiIiIiInpfmUk0PTw8EBoaKu7LZDIMHz4crq6ukEgkYrmbmxsyMjLw8OFDpKamIjs7G25ubuJxTU1NuLi4ID4+HgAQHx+Phg0bKvTl6uqqsC8IwkfjS09Px6NHjxT6yo8nLi6u6BcKQEtLC46OjsU6J19gYCDGjBmjEJe5uXmJ2iIiIiIiorKpzCSaMpkMVlZWpdK3jY0Nbt68+cntqKm9faT23cQ1OztbqZ6Ojo5C8lwcUqkUUqm0ZAESERERERGhDC0GVBB7e3ucOXNGIXGLjo6Grq4uqlSpgho1akBLSwvR0dHi8ezsbJw/fx41a9YU24iJiVFo9+zZswr7vXv3xq1bt7Br1y6lGARBQFpaGvT09FC5cmWFvvLjye+rQoUKAICUlBTxeGxsbJGuVUtLC7m5uUWqS0RERERE9CnKdKLp7++PBw8eYOTIkbh58yZ27dqF6dOnY8yYMVBTU4NMJsOwYcMwfvx4HDx4EDdu3MCgQYOQmZkJPz8/AMDQoUORmJiI8ePHIyEhAb/99hvCw8MV+vH29kaPHj3Qq1cvzJ49GxcuXMC9e/ewd+9etGzZEseOHQMAjB8/HnPnzsWWLVuQkJCASZMmITY2FqNHjwYAWFlZwdzcHEFBQUhMTMS+ffvEZ0M/xsLCAleuXEFCQgL+/vvvAkdCiYiIiIiIVKHMTJ0tiJmZGfbv34/x48fDyckJRkZG8PPzw5QpU8Q6P/30E/Ly8tCnTx+8fPkSzs7OOHToEAwNDQEAVatWRWRkJL7//nssW7YMLi4umD17NgYMGCC2IZFI8Ntvv2H16tVYt24dZs2aBQ0NDVhbW6Nv377w9PQEAIwaNQppaWkYO3Ysnj59ipo1a2L37t3iirOamprYtGkThg0bBkdHRzRo0AAzZ85E9+7dP3qtgwYNQlRUFJydnZGRkYFjx47B3d1dhXeTiIiIiIjoLYlQlJVqqMxKT0+Hvr4+pp+4C225bmmHQ0REBACYVLd8aYdARFTm5OcG+Y/+fUiZnjpLREREREREqlemp85S0Y1xMv7oXy2IiIiIiIgAjmgSERERERGRijHRJCIiIiIiIpVioklEREREREQqxUSTiIiIiIiIVIqLAVGRLIp7Bm35m9IOg4iIvmJ8JQkRUdnBEU0iIiIiIiJSKSaaREREREREpFJMNImIiIiIiEilmGh+4c6cOQN1dXW0a9eutEMhIiIiIiIqEiaaX7iwsDCMHDkSJ06cwKNHj0o7HCIiIiIioo9iovkFy8jIwJYtWzBs2DC0a9cO4eHhCsd3794Na2traGtrw8PDAxEREZBIJEhNTRXrnDp1Ck2aNIGOjg7Mzc0xatQovHr16t+9ECIiIiIiKlOYaH7Btm7dCjs7O9ja2uK7777DunXrIAgCACApKQnffvstOnfujLi4OAwZMgSTJ09WOP/OnTvw8vJCt27dcOXKFWzZsgWnTp3CiBEjCu0zKysL6enpChsREREREVFxMNH8goWFheG7774DAHh5eSEtLQ3Hjx8HAKxatQq2traYP38+bG1t0bNnT/j6+iqcP2fOHPj4+CAgIADW1tZo3Lgxli5dig0bNuD169cF9jlnzhzo6+uLm7m5+We9RiIiIiIi+u9hovmFSkhIQExMDHr16gUA0NDQQI8ePRAWFiYeb9CggcI5Li4uCvtxcXEIDw+HXC4XN09PT+Tl5SEpKanAfgMDA5GWliZuDx48+AxXR0RERERE/2UapR0AFSwsLAw5OTmoXLmyWCYIAqRSKZYvX16kNjIyMjBkyBCMGjVK6VjVqlULPEcqlUIqlZYsaCIiIiIiIjDR/CLl5ORgw4YNWLhwIVq3bq1wrHPnzti0aRNsbW2xf/9+hWPnz59X2K9Xrx5u3LgBKyurzx4zERERERFRPiaaX6C9e/fixYsX8PPzg76+vsKxbt26ISwsDFu3bsWiRYswceJE+Pn5ITY2VlyVViKRAAAmTpyIRo0aYcSIERg4cCBkMhlu3LiBI0eOFHlUlIiIiIiIqLj4jOYXKCwsDC1btlRKMoG3ieaFCxfw8uVLbN++HTt27ICjoyNCQ0PFVWfzp746Ojri+PHjuHXrFpo0aYK6deti2rRpCtNxiYiIiIiIVE0i5L8vg756s2bNwsqVK1W6gE96ejr09fUx/cRdaMt1VdYuERGVPZPqli/tEIiI6BPk5wZpaWnQ09P7YF1Onf2KrVixAg0aNICxsTGio6Mxf/78D74jk4iIiIiI6N/ARPMrlpiYiJkzZ+L58+eoWrUqxo4di8DAwNIOi4iIiIiIyjhOnaUPKs7wOBERERER/XcVJzfgYkBERERERESkUkw0iYiIiIiISKWYaBIREREREZFKcTEgKpJFcc+gLX9T2mEQEVER8DUiRERU2jiiSURERERERCrFRJOIiIiIiIhUiokmERERERERqRQTzS/Y//73PwwbNgxVq1aFVCqFiYkJPD09ER0dXdqhERERERERFYqLAX3BunXrhjdv3iAiIgKWlpZ48uQJjh49imfPnpV2aERERERERIXiiOYXKjU1FSdPnsTcuXPh4eGBatWqwcXFBYGBgejYsSMAQCKRIDQ0FG3atIGOjg4sLS2xfft2hXYmTpwIGxsblCtXDpaWlpg6dSqys7NL45KIiIiIiKiMYKL5hZLL5ZDL5fj999+RlZVVaL2pU6eiW7duiIuLg4+PD3r27In4+HjxuK6uLsLDw3Hjxg0sWbIEa9aswc8//1xoe1lZWUhPT1fYiIiIiIiIikMiCIJQ2kFQwSIjIzFo0CD8888/qFevHpo1a4aePXvC0dERwNsRzaFDhyI0NFQ8p1GjRqhXrx5WrFhRYJsLFizA5s2bceHChQKPBwUFITg4WKl8+om70JbrquCqiIjoc+N7NImI6HNIT0+Hvr4+0tLSoKen98G6HNH8gnXr1g2PHj3C7t274eXlhaioKNSrVw/h4eFiHVdXV4VzXF1dFUY0t2zZAjc3N5iYmEAul2PKlCm4f/9+oX0GBgYiLS1N3B48eKDy6yIiIiIiov82JppfOG1tbbRq1QpTp07F6dOn4evri+nTpxfp3DNnzsDHxwdt27bF3r17cfnyZUyePBlv3rwp9BypVAo9PT2FjYiIiIiIqDiYaH5latasiVevXon7Z8+eVTh+9uxZ2NvbAwBOnz6NatWqYfLkyXB2doa1tTXu3bv3r8ZLRERERERlD19v8oV69uwZunfvjgEDBsDR0RG6urq4cOEC5s2bh06dOon1tm3bBmdnZ3zzzTfYuHEjYmJiEBYWBgCwtrbG/fv3sXnzZjRo0AD79u3Dzp07S+uSiIiIiIiojGCi+YWSy+Vo2LAhfv75Z9y5cwfZ2dkwNzfHoEGD8MMPP4j1goODsXnzZvj7+8PU1BSbNm1CzZo1AQAdO3bE999/jxEjRiArKwvt2rXD1KlTERQUVEpXRUREREREZQFXnf2KSSQS7Ny5E507d/5sfeSvLMVVZ4mIvh5cdZaIiD4HrjpLREREREREpYaJJhEREREREakUn9H8iv2bs57HOBnzVSdERERERFQkHNEkIiIiIiIilWKiSURERERERCrFRJOIiIiIiIhUis9oUpEsinsGbfmb0g6DiOirxteOEBFRWcERTSIiIiIiIlIpJppERERERESkUkw0iYiIiIiISKW+ukRTIpHg999/L+0wAHxZsXyIhYUFFi9eLO5/LXETEREREdHX6YtLNP/3v/9h2LBhqFq1KqRSKUxMTODp6Yno6OjSDk1JSkoK2rRpo9I2w8PDIZFIYG9vr3Rs27ZtkEgksLCwUGmfREREREREqvTFrTrbrVs3vHnzBhEREbC0tMSTJ09w9OhRPHv2rLRDU2JiYvJZ2pXJZHj69CnOnDkDV1dXsTwsLAxVq1b9LH0SERERERGpyhc1opmamoqTJ09i7ty58PDwQLVq1eDi4oLAwEB07NhRrPf333+jS5cuKFeuHKytrbF7926Fdo4fPw4XFxdIpVKYmppi0qRJyMnJEY+7u7tjxIgRGDFiBPT19VG+fHlMnToVgiCIdSwsLPDjjz+iV69ekMlkMDMzQ0hIiEI/705BTU5OhkQiwY4dO+Dh4YFy5crByckJZ86cUThnzZo1MDc3R7ly5dClSxcsWrQIBgYGCnU0NDTQu3dvrFu3Tix7+PAhoqKi0Lt3b4W6d+7cQadOnVCpUiXI5XI0aNAAf/zxR9Fv+nuysrKQnp6usBERERERERXHF5VoyuVyyOVy/P7778jKyiq0XnBwMLy9vXHlyhW0bdsWPj4+eP78OQDgr7/+Qtu2bdGgQQPExcUhNDQUYWFhmDlzpkIbERER0NDQQExMDJYsWYJFixZh7dq1CnXmz58PJycnXL58GZMmTcLo0aNx5MiRD17D5MmTMW7cOMTGxsLGxga9evUSk9zo6GgMHToUo0ePRmxsLFq1aoVZs2YV2M6AAQOwdetWZGZmAng7pdbLywuVKlVSqJeRkYG2bdvi6NGjuHz5Mry8vNChQwfcv3//g3EWZs6cOdDX1xc3c3PzErVDRERERERl1xeVaGpoaCA8PBwREREwMDCAm5sbfvjhB1y5ckWhnq+vL3r16gUrKyvMnj0bGRkZiImJAQCsWLEC5ubmWL58Oezs7NC5c2cEBwdj4cKFyMvLE9swNzfHzz//DFtbW/j4+GDkyJH4+eefFfpxc3PDpEmTYGNjg5EjR+Lbb79VqvO+cePGoV27drCxsUFwcDDu3buH27dvAwCWLVuGNm3aYNy4cbCxsYG/v3+hz3jWrVsXlpaW2L59OwRBQHh4OAYMGKBUz8nJCUOGDEHt2rVhbW2NH3/8ETVq1FAa5S2qwMBApKWliduDBw9K1A4REREREZVdX1SiCbx9RvPRo0fYvXs3vLy8EBUVhXr16iE8PFys4+joKH4tk8mgp6eHp0+fAgDi4+Ph6uoKiUQi1nFzc0NGRgYePnwoljVq1EihjqurKxITE5Gbm6tQ9i5XV1fEx8d/MP53YzM1NQUAMbaEhAS4uLgo1H9//10DBgzA+vXrcfz4cbx69Qpt27ZVqpORkYFx48bB3t4eBgYGkMvliI+PL/GIplQqhZ6ensJGRERERERUHF9cogkA2traaNWqFaZOnYrTp0/D19cX06dPF49ramoq1JdIJAqjlaXp3djyE9mSxubj44OzZ88iKCgIffr0gYaG8tpN48aNw86dOzF79mycPHkSsbGxcHBwwJs3b0p2AURERERERJ/oi0w031ezZk28evWqSHXt7e1x5swZhYV9oqOjoauriypVqohl586dUzjv7NmzsLa2hrq6ukLZ+3UKeu1IUdna2uL8+fMKZe/vv8vIyAgdO3bE8ePHC5w2C7y9Nl9fX3Tp0gUODg4wMTFBcnJyiWMkIiIiIiL6VF9Uovns2TM0b94cv/76K65cuYKkpCRs27YN8+bNQ6dOnYrUhr+/Px48eICRI0fi5s2b2LVrF6ZPn44xY8ZATe3/Lvf+/fsYM2YMEhISsGnTJixbtgyjR49WaCs6Ohrz5s3DrVu3EBISgm3btinVKY6RI0di//79WLRoERITE7Fq1SocOHBAYQrv+8LDw/H333/Dzs6uwOPW1tbYsWMHYmNjERcXh969e38xo7tERERERFQ2fVHv0ZTL5WjYsCF+/vln3LlzB9nZ2TA3N8egQYPwww8/FKkNMzMz7N+/H+PHj4eTkxOMjIzg5+eHKVOmKNTr27cv/vnnH7i4uEBdXR2jR4/G4MGDFeqMHTsWFy5cQHBwMPT09LBo0SJ4enqW+Prc3NywcuVKBAcHY8qUKfD09MT333+P5cuXF3qOjo4OdHR0Cj2+aNEiDBgwAI0bN0b58uUxceJEvpKEiIiIiIhKlUR4d45pGeHu7o46depg8eLFhdaxsLBAQEAAAgICPmssgwYNws2bN3Hy5MnP2k9JpaenQ19fH9NP3IW2XLe0wyEi+qpNqlu+tEMgIiIqsfzcIC0t7aOLhn5RI5plwYIFC9CqVSvIZDIcOHAAERERWLFiRWmHRUREREREpDJMNP9lMTExmDdvHl6+fAlLS0ssXboUAwcOLO2wPmqMkzFfdUJEREREREVSJqfOUtEVZ3iciIiIiIj+u4qTG3xRq84SERERERHR14+JJhEREREREakUn9GkIlkU9wza8jelHQYRkRKu5EpERPTl4YgmERERERERqRQTTSIiIiIiIlIpJppERERERESkUkw0vyDu7u4ICAgo7TCIiIiIiIg+SZlLNB8/foyRI0fC0tISUqkU5ubm6NChA44ePfpZ+83NzcVPP/0EOzs76OjowMjICA0bNsTatWs/a79ERERERET/tjK16mxycjLc3NxgYGCA+fPnw8HBAdnZ2Th06BCGDx+OmzdvKp2TnZ0NTU3NT+47ODgYq1atwvLly+Hs7Iz09HRcuHABL168+OS2iYiIiIiIviRlakTT398fEokEMTEx6NatG2xsbFCrVi2MGTMGZ8+eBQBIJBKEhoaiY8eOkMlkmDlzJqysrLBgwQKFtmJjYyGRSHD79m0AwP3799GpUyfI5XLo6enB29sbT548Eevv3r0b/v7+6N69O6pXrw4nJyf4+flh3LhxhcablZWFcePGwczMDDKZDA0bNkRUVJRCnVOnTqFJkybQ0dGBubk5Ro0ahVevXonHLSws8OOPP6JXr16QyWQwMzNDSEjIp95KIiIiIiKiQpWZRPP58+c4ePAghg8fDplMpnTcwMBA/DooKAhdunTB1atX4efnhwEDBmD9+vUK9devX4+mTZvCysoKeXl56NSpE54/f47jx4/jyJEjuHv3Lnr06CHWNzExwZ9//on//e9/RY55xIgROHPmDDZv3owrV66ge/fu8PLyQmJiIgDgzp078PLyQrdu3XDlyhVs2bIFp06dwogRIxTamT9/PpycnHD58mVMmjQJo0ePxpEjRwrsMysrC+np6QobERERERFRcUgEQRBKO4h/Q0xMDBo2bIgdO3agS5cuhdaTSCQICAjAzz//LJY9evQIVatWxenTp+Hi4oLs7GxUrlwZCxYsQL9+/XDkyBG0adMGSUlJMDc3BwDcuHEDtWrVQkxMDBo0aIAbN27g22+/RUJCAmrVqoXGjRujU6dOaNOmjdiPu7s76tSpg8WLF+P+/fuwtLTE/fv3UblyZbFOy5Yt4eLigtmzZ2PgwIFQV1fHqlWrxOOnTp1Cs2bN8OrVK2hra8PCwgL29vY4cOCAWKdnz55IT0/H/v37la4/KCgIwcHBSuXTT9yFtly3iHebiOjfM6lu+dIOgYiIqExIT0+Hvr4+0tLSoKen98G6ZWZEszj5tLOzs8J+5cqV0a5dO6xbtw4AsGfPHmRlZaF79+4AgPj4eJibm4tJJgDUrFkTBgYGiI+PF/evXbuGs2fPYsCAAXj69Ck6dOiAgQMHFhjD1atXkZubCxsbG8jlcnE7fvw47ty5AwCIi4tDeHi4wnFPT0/k5eUhKSlJbMvV1VWhbVdXVzGu9wUGBiItLU3cHjx4UOT7RkREREREBJShxYCsra0hkUgKXPDnfQVNrR04cCD69OmDn3/+GevXr0ePHj1Qrly5YsWgpqaGBg0aoEGDBggICMCvv/6KPn36YPLkyahevbpC3YyMDKirq+PixYtQV1dXOCaXy8U6Q4YMwahRo5T6qlq1arFiyyeVSiGVSkt0LhEREREREVCGEk0jIyN4enoiJCQEo0aNUkomU1NTFZ7TfF/btm0hk8kQGhqKgwcP4sSJE+Ixe3t7PHjwAA8ePFCYOpuamoqaNWsW2mb+sXcX78lXt25d5Obm4unTp2jSpEmB59erVw83btyAlZVVoX0AEBc6enff3t7+g+cQERERERGVVJmZOgsAISEhyM3NhYuLCyIjI5GYmIj4+HgsXbpUaXrp+9TV1eHr64vAwEBYW1sr1G/ZsiUcHBzg4+ODS5cuISYmBn379kWzZs3Eabjffvstfv75Z5w7dw737t1DVFQUhg8fDhsbG9jZ2Sn1Z2NjAx8fH/Tt2xc7duxAUlISYmJiMGfOHOzbtw8AMHHiRJw+fRojRoxAbGwsEhMTsWvXLqXFgKKjozFv3jzcunULISEh2LZtG0aPHv2pt5OIiIiIiKhAZSrRtLS0xKVLl+Dh4YGxY8eidu3aaNWqFY4ePYrQ0NCPnu/n54c3b96gf//+CuUSiQS7du2CoaEhmjZtipYtW8LS0hJbtmwR63h6emLPnj3o0KEDbGxs0K9fP9jZ2eHw4cPQ0Ch4YHn9+vXo27cvxo4dC1tbW3Tu3Bnnz58Xp8U6Ojri+PHjuHXrFpo0aYK6deti2rRpCosHAcDYsWNx4cIF1K1bFzNnzsSiRYvg6elZ3NtHRERERERUJGVm1VlVOHnyJFq0aIEHDx6gUqVKpR1OkVhYWCAgIAABAQElOj9/ZSmuOktEXyquOktERPTvKM6qs2XmGc1PkZWVhf/9738ICgpC9+7dv5okk4iIiIiIqDQw0SyCTZs2wc/PD3Xq1MGGDRtKO5xSMcbJ+KN/tSAiIiIiIgI4dZY+ojjD40RERERE9N9VnNygTC0GRERERERERJ8fE00iIiIiIiJSKT6jSUWyKO4ZtOVvSjsMIiqjuLIsERHR14UjmkRERERERKRSTDSJiIiIiIhIpZhoEhERERERkUox0fyPc3d3R0BAQGmHQUREREREZQgTzSLq0KEDvLy8Cjx28uRJSCQSXLlyBRKJRNyMjIzQrFkznDx5UqF+UFCQWEdDQwPly5dH06ZNsXjxYmRlZf0bl0NERERERPTZMNEsIj8/Pxw5cgQPHz5UOrZ+/Xo4OzuLLy39448/kJKSghMnTqBy5cpo3749njx5onBOrVq1kJKSgvv37+PYsWPo3r075syZg8aNG+Ply5f/yjURERERERF9Dkw0i6h9+/aoUKECwsPDFcozMjKwbds2+Pn5iWXGxsYwMTFB7dq18cMPPyA9PR3nzp1TOE9DQwMmJiaoXLkyHBwcMHLkSBw/fhzXrl3D3LlzxXpZWVkYN24czMzMIJPJ0LBhQ0RFRSm0FR0dDXd3d5QrVw6Ghobw9PTEixcvCryOffv2QV9fHxs3bvy0G0JERERERFQIJppFpKGhgb59+yI8PByCIIjl27ZtQ25uLnr16qV0zj///IMNGzYAALS0tD7ah52dHdq0aYMdO3aIZSNGjMCZM2ewefNmXLlyBd27d4eXlxcSExMBALGxsWjRogVq1qyJM2fO4NSpU+jQoQNyc3OV2v/tt9/Qq1cvbNy4ET4+PgXGkJWVhfT0dIWNiIiIiIioODRKO4CvyYABAzB//nwcP34c7u7uAN5Om+3WrRv09fXFUcTGjRtDTU0NmZmZEAQB9evXR4sWLYrUh52dHQ4fPgwAuH//PtavX4/79++jcuXKAIBx48bh4MGDWL9+PWbPno158+bB2dkZK1asENuoVauWUrshISGYPHky9uzZg2bNmhXa/5w5cxAcHFykWImIiIiIiArCEc1isLOzQ+PGjbFu3ToAwO3bt3Hy5EmFabMAsGXLFly+fBmRkZGwsrJCeHg4NDU1i9SHIAiQSCQAgKtXryI3Nxc2NjaQy+Xidvz4cdy5cwfA/41ofsj27dvx/fff48iRIx9MMgEgMDAQaWlp4vbgwYMixU1ERERERJSPI5rF5Ofnh5EjRyIkJATr169HjRo1lJI3c3NzWFtbw9raGjk5OejSpQuuXbsGqVT60fbj4+NRvXp1AG+f/1RXV8fFixehrq6uUE8ulwMAdHR0Ptpm3bp1cenSJaxbtw7Ozs5iIlsQqVRapDiJiIiIiIgKwxHNYvL29oaamhp+++03bNiwAQMGDPhg4vbtt99CQ0NDYWprYW7evImDBw+iW7duAN4miLm5uXj69CmsrKwUNhMTEwCAo6Mjjh49+sF2a9SogWPHjmHXrl0YOXJkMa6WiIiIiIio+JhoFpNcLkePHj0QGBiIlJQU+Pr6frC+RCLBqFGj8NNPPyEzM1Msz8nJwePHj/Ho0SNcvXoVy5YtQ7NmzVCnTh2MHz8eAGBjYwMfHx/07dsXO3bsQFJSEmJiYjBnzhzs27cPwNuprufPn4e/vz+uXLmCmzdvIjQ0FH///bdCHDY2Njh27BgiIyMREBCg0ntCRERERET0LpUlmqmpqapq6ovn5+eHFy9ewNPTU1yk50P69euH7OxsLF++XCy7fv06TE1NUbVqVbi7u2Pr1q0IDAzEyZMnxWmxwNvFhvr27YuxY8fC1tYWnTt3xvnz51G1alUAbxPIw4cPIy4uDi4uLnB1dcWuXbugoaE8K9rW1hZ//vknNm3ahLFjx6rgThARERERESmTCO++q6OI5s6dCwsLC/To0QPA2+mkkZGRMDExwf79++Hk5KTyQKl0pKenQ19fH9NP3IW2XLe0wyGiMmpS3fKlHQIREVGZl58bpKWlQU9P74N1SzSiuXLlSpibmwMAjhw5giNHjuDAgQNo06aNOO2TiIiIiIiIyqYSrTr7+PFjMdHcu3cvvL290bp1a1hYWKBhw4YqDZC+DGOcjD/6VwsiIiIiIiKghCOahoaG4vsVDx48iJYtWwJ4+w7I3Nxc1UVHREREREREX50SjWh27doVvXv3hrW1NZ49e4Y2bdoAAC5fvgwrKyuVBkhERERERERflxIlmj///DMsLCzw4MEDzJs3T1wlNSUlBf7+/ioNkIiIiIiIiL4uJVp1lsqO4qwsRURERERE/13FyQ1KNKIJAL/88gtWrVqFu3fv4syZM6hWrRoWL16M6tWro1OnTiVtlr5Qi+KeQVv+prTDIKIvFF8/QkRERO8q0WJAoaGhGDNmDNq0aYPU1FRxASADAwMsXrxYlfERERERERHRV6ZEieayZcuwZs0aTJ48Gerq6mK5s7Mzrl69qrLgiIiIiIiI6OtTokQzKSkJdevWVSqXSqV49erVJwdFhZNIJPj9999LOwwiIiIiIqJClSjRrF69OmJjY5XKDx48CHt7+0+NCQDg6+sLiUQCiUQCTU1NVK9eHRMmTMDr169V0v6n2LlzJxo1agR9fX3o6uqiVq1aCAgI+Ff6TklJEV8nQ0RERERE9CUq0WJAY8aMwfDhw/H69WsIgoCYmBhs2rQJc+bMwdq1a1UWnJeXF9avX4/s7GxcvHgR/fr1g0Qiwdy5c1XWR3EdPXoUPXr0wKxZs9CxY0dIJBLcuHEDR44c+aR2s7Ozoamp+dF6JiYmn9QPERERERHR51aiEc2BAwdi7ty5mDJlCjIzM9G7d2+EhoZiyZIl6Nmzp8qCk0qlMDExgbm5OTp37oyWLVuKCV1WVhZGjRqFihUrQltbG9988w3Onz+vcP7x48fh4uICqVQKU1NTTJo0CTk5OeJxd3d3jBw5EgEBATA0NESlSpWwZs0avHr1Cv3794euri6srKxw4MAB8Zw9e/bAzc0N48ePh62tLWxsbNC5c2eEhIQo9L1r1y7Uq1cP2trasLS0RHBwsELfEokEoaGh6NixI2QyGX788UdUqVIFoaGhCu1cvnwZampquHfvnnjeu1NnHz58iF69esHIyAgymQzOzs44d+5ckeMgIiIiIiJStWInmjk5OdiwYQNatmyJxMREZGRk4PHjx3j48CH8/Pw+R4wAgGvXruH06dPQ0tICAEyYMAGRkZGIiIjApUuXYGVlBU9PTzx//hwA8Ndff6Ft27Zo0KAB4uLiEBoairCwMMycOVOh3YiICJQvXx4xMTEYOXIkhg0bhu7du6Nx48a4dOkSWrdujT59+iAzMxPA2xHF69ev49q1a4XGevLkSfTt2xejR4/GjRs3sGrVKoSHh2PWrFkK9YKCgtClSxdcvXoVAwcORK9evfDbb78p1Nm4cSPc3NxQrVo1pX4yMjLQrFkz/PXXX9i9ezfi4uIwYcIE5OXlFSuOd2VlZSE9PV1hIyIiIiIiKg6JIAhCcU8qV64c4uPjC0x+VMXX1xe//vortLW1kZOTg6ysLKipqWHr1q3w8vKCoaEhwsPD0bt3bwBvp55aWFggICAA48ePx+TJkxEZGYn4+HhIJBIAwIoVKzBx4kSkpaVBTU0N7u7uyM3NxcmTJwEAubm50NfXR9euXbFhwwYAwOPHj2FqaoozZ86gUaNGePXqFby9vbF//35Uq1YNjRo1QuvWreHj4wOpVAoAaNmyJVq0aIHAwEDxen799VdMmDABjx49AvB2ZDIgIAA///yzWCc2Nhb16tVDcnIyqlatiry8PFStWhVTpkzB0KFDxfN27tyJzp07Y/Xq1Rg3bhySk5NhZGSkdA+LEsf7goKCEBwcrFQ+/cRdaMt1i/jdI6Kyhu/RJCIi+u9LT0+Hvr4+0tLSoKen98G6JZo66+LigsuXL5couOLw8PBAbGwszp07h379+qF///7o1q0b7ty5g+zsbLi5uYl1NTU14eLigvj4eABAfHw8XF1dxSQTANzc3JCRkYGHDx+KZY6OjuLX6urqMDY2hoODg1hWqVIlAMDTp08BADKZDPv27cPt27cxZcoUyOVyjB07Fi4uLuKoZ1xcHGbMmAG5XC5ugwYNQkpKilgHePs6mHfVqVMH9vb24qjm8ePH8fTpU3Tv3r3A+xMbG4u6desWmGQWJ453BQYGIi0tTdwePHhQYD0iIiIiIqLClGgxIH9/f4wdOxYPHz5E/fr1IZPJFI6/m7x9CplMBisrKwDAunXr4OTkhLCwMDRo0EAl7QNQWoAnf5Xbd/cBiNNR89WoUQM1atTAwIEDMXnyZNjY2GDLli3o378/MjIyEBwcjK5duyr1p62trXB97/Px8cFvv/2GSZMm4bfffoOXlxeMjY0LjF1HR+eD11bUON4llUrFkVkiIiIiIqKSKFGimb/gz6hRo8QyiUQCQRAgkUiQm5urmujeoaamhh9++AFjxozB7du3oaWlhejoaHH6bnZ2Ns6fPy++ZsTe3h6RkZFiTAAQHR0NXV1dVKlSRaWxWVhYoFy5cuI7ROvVq4eEhAQxSS6O3r17Y8qUKbh48SK2b9+OlStXFlrX0dERa9euxfPnzwsc1fyUOIiIiIiIiEqqRIlmUlKSquMoku7du2P8+PEIDQ3FsGHDMH78eBgZGaFq1aqYN28eMjMzxQWJ/P39sXjxYowcORIjRoxAQkICpk+fjjFjxkBNrUQzhgG8fYYxMzMTbdu2RbVq1ZCamoqlS5ciOzsbrVq1AgBMmzYN7du3R9WqVfHtt99CTU0NcXFxuHbtmtJiRO+zsLBA48aN4efnh9zcXHTs2LHQur169cLs2bPRuXNnzJkzB6amprh8+TIqV64MV1fXT4qDiIiIiIiopEqUaH7ORYA+RENDAyNGjMC8efOQlJSEvLw89OnTBy9fvoSzszMOHToEQ0NDAICZmRn279+P8ePHw8nJCUZGRvDz88OUKVM+KYZmzZohJCQEffv2xZMnT2BoaIi6devi8OHDsLW1BQB4enpi7969mDFjBubOnQtNTU3Y2dlh4MCBRerDx8cH/v7+6Nu37wenx2ppaeHw4cMYO3Ys2rZti5ycHNSsWVN81cqnxkFERERERFQSJVp1Nn9F1sL07du3xAHRlyV/ZSmuOktEH8JVZ4mIiP77irPqbIlGNEePHq2wn52djczMTGhpaaFcuXJMNImIiIiIiMqwEj2s+OLFC4UtIyMDCQkJ+Oabb7Bp0yZVx0hERERERERfkRJNnS3MhQsX8N133+HmzZuqapJKWXGGx4mIiIiI6L+rOLlByZdfLYCGhgYePXqkyiaJiIiIiIjoK1OiZzR3796tsC8IAlJSUrB8+XK4ubmpJDAiIiIiIiL6OpUo0ezcubPCvkQiQYUKFdC8eXMsXLhQFXERERERERHRV6pEiWZeXp6q46Av3KK4Z9CWvyntMIioCPiqESIiIiptJXpGc8aMGcjMzFQq/+effzBjxoxPDoqIiIiIiIi+XiVKNIODg5GRkaFUnpmZieDg4E8OioiIiIiIiL5eJUo0BUGARCJRKo+Li4ORkdEnB/UlCQ8Ph4GBwQfrBAUFoU6dOv9KPCUhkUjw+++/l3YYRERERERURhQr0TQ0NISRkREkEglsbGxgZGQkbvr6+mjVqhW8vb0/V6wl8vjxY4wcORKWlpaQSqUwNzdHhw4dcPToUZX1MW7cuGK35+7uDolEAolEAqlUCjMzM3To0AE7duxQWVxERERERESloViLAS1evBiCIGDAgAEIDg6Gvr6+eExLSwsWFhZwdXVVeZAllZycDDc3NxgYGGD+/PlwcHBAdnY2Dh06hOHDh+PmzZsq6Ucul0Mulxf7vEGDBmHGjBnIycnBw4cPsXPnTvTs2RO+vr5YvXq1SmIjIiIiIiL6txVrRLNfv37w9fXFsWPHMGzYMPTr10/cevXq9UUlmQDg7+8PiUSCmJgYdOvWDTY2NqhVqxbGjBmDs2fPAgAWLVoEBwcHyGQymJubw9/fv8DnT3///XdYW1tDW1sbnp6eePDggXjs/amzvr6+6Ny5MxYsWABTU1MYGxtj+PDhyM7OVmizXLlyMDExQZUqVdCoUSPMnTsXq1atwpo1a/DHH3+I9R48eABvb28YGBjAyMgInTp1QnJyskJb69atQ61atSCVSmFqaooRI0YUel+mT58OU1NTXLlypTi3k4iIiIiIqEhK9Ixms2bNoKmpCQB4/fo10tPTFbYvwfPnz3Hw4EEMHz4cMplM6Xj+c5dqampYunQprl+/joiICPz555+YMGGCQt3MzEzMmjULGzZsQHR0NFJTU9GzZ88P9n/s2DHcuXMHx44dQ0REBMLDwxEeHv7RuPv16wdDQ0NxCm12djY8PT2hq6uLkydPIjo6GnK5HF5eXnjz5u3rRkJDQzF8+HAMHjwYV69exe7du2FlZaXUtiAIGDlyJDZs2ICTJ0/C0dFRqU5WVtYX+f0kIiIiIqKvR4neo5mZmYkJEyZg69atePbsmdLx3NzcTw7sU92+fRuCIMDOzu6D9QICAsSvLSwsMHPmTAwdOhQrVqwQy7Ozs7F8+XI0bNgQABAREQF7e3vExMTAxcWlwHYNDQ2xfPlyqKurw87ODu3atcPRo0cxaNCgD8ajpqYGGxsbccRyy5YtyMvLw9q1a8UFmNavXw8DAwNERUWhdevWmDlzJsaOHYvRo0eL7TRo0ECh3ZycHHz33Xe4fPkyTp06BTMzswL7nzNnDlcOJiIiIiKiT1KiEc3x48fjzz//RGhoKKRSKdauXYvg4GBUrlwZGzZsUHWMJSIIQpHq/fHHH2jRogXMzMygq6uLPn364NmzZwrvCdXQ0FBI3Ozs7GBgYID4+PhC261VqxbU1dXFfVNTUzx9+rTIsecnlXFxcbh9+zZ0dXXFZ0GNjIzw+vVr3LlzB0+fPsWjR4/QokWLD7b5/fff49y5czhx4kShSSYABAYGIi0tTdzenSJMRERERERUFCVKNPfs2YMVK1agW7du0NDQQJMmTTBlyhTMnj0bGzduVHWMJWJtbQ2JRPLBBX+Sk5PRvn17ODo6IjIyEhcvXkRISAgAiNNSSyp/anE+iUSCvLy8j56Xm5uLxMREVK9eHQCQkZGB+vXrIzY2VmG7desWevfuDR0dnSLF06pVK/z11184dOjQB+tJpVLo6ekpbERERERERMVRokTz+fPnsLS0BADo6enh+fPnAIBvvvkGJ06cUF10n8DIyAienp4ICQnBq1evlI6npqbi4sWLyMvLw8KFC9GoUSPY2Njg0aNHSnVzcnJw4cIFcT8hIQGpqamwt7dXedwRERF48eIFunXrBgCoV68eEhMTUbFiRVhZWSls+vr60NXVhYWFxUdfr9KxY0f89ttvGDhwIDZv3qzyuImIiIiIiPKVKNG0tLREUlISgLfTSLdu3Qrg7Uhn/iI7X4KQkBDk5ubCxcUFkZGRSExMRHx8PJYuXQpXV1dYWVkhOzsby5Ytw927d/HLL79g5cqVSu1oampi5MiROHfuHC5evAhfX180atSo0OcziyozMxOPHz/Gw4cPcfbsWUycOBFDhw7FsGHD4OHhAQDw8fFB+fLl0alTJ5w8eRJJSUmIiorCqFGj8PDhQwBvV71duHAhli5disTERFy6dAnLli1T6q9Lly745Zdf0L9/f2zfvv2TYiciIiIiIipMiRLN/v37Iy4uDgAwadIkhISEQFtbG99//z3Gjx+v0gA/haWlJS5dugQPDw+MHTsWtWvXRqtWrXD06FGEhobCyckJixYtwty5c1G7dm1s3LgRc+bMUWqnXLlymDhxInr37g03NzfI5XJs2bLlk+Nbs2YNTE1NUaNGDXTt2hU3btzAli1bFBYiKleuHE6cOIGqVauia9eusLe3h5+fH16/fi1Oa+3Xrx8WL16MFStWoFatWmjfvj0SExML7PPbb79FREQE+vTpI65sS0REREREpEoSoair5nzAvXv3cPHiRVhZWRX4ygz6eqWnp0NfXx/TT9yFtly3tMMhoiKYVLd8aYdARERE/0H5uUFaWtpH13Ip0etN3vX69WtUq1YN1apV+9SmiIiIiIiI6D+gRFNnc3Nz8eOPP8LMzAxyuRx3794FAEydOhVhYWEqDZCIiIiIiIi+LiUa0Zw1axYiIiIwb948DBo0SCyvXbs2Fi9eDD8/P5UFSF+GMU7GfNUJEREREREVSYlGNDds2IDVq1fDx8cH6urqYrmTk9MH31tJRERERERE/30lSjT/+usvWFlZKZXn5eUhOzv7k4MiIiIiIiKir1eJEs2aNWvi5MmTSuXbt29H3bp1PzkoIiIiIiIi+nqV6BnNadOmoV+/fvjrr7+Ql5eHHTt2ICEhARs2bMDevXtVHSN9ARbFPYO2/E1ph0FfIL5Kg4iIiIjeV6wRzbt370IQBHTq1Al79uzBH3/8AZlMhmnTpiE+Ph579uxBq1atPlesRERERERE9BUo1oimtbU1UlJSULFiRTRp0gRGRka4evUqKlWq9LniIyIiIiIioq9MsUY0BUFQ2D9w4ABevXql0oCIiIiIiIjo61aixYDyvZ94lhXh4eEwMDD4YJ2goCDUqVNHpf0mJydDIpEgNja20DpRUVGQSCRITU1Vad9ERERERERFVaxEUyKRQCKRKJV9bR4/foyRI0fC0tISUqkU5ubm6NChA44ePaqyPsaNG1fs9pKSktC7d29UrlwZ2traqFKlCjp16iS+m9Tc3BwpKSmoXbu2yuIkIiIiIiJStWI9oykIAnx9fSGVSgEAr1+/xtChQyGTyRTq7dixQ3URqlhycjLc3NxgYGCA+fPnw8HBAdnZ2Th06BCGDx8uJnWfSi6XQy6XF7l+dnY2WrVqBVtbW+zYsQOmpqZ4+PAhDhw4II5Oqqurw8TERCXxERERERERfS7FGtHs168fKlasCH19fejr6+O7775D5cqVxf387Uvm7+8PiUSCmJgYdOvWDTY2NqhVqxbGjBmDs2fPAgAWLVoEBwcHyGQymJubw9/fHxkZGUpt/f7777C2toa2tjY8PT3x4MED8dj7U2d9fX3RuXNnLFiwAKampjA2Nsbw4cORnZ0NALh+/Tru3LmDFStWoFGjRqhWrRrc3Nwwc+ZMNGrUCEDBU2f3798PGxsb6OjowMPDA8nJyUpxnjp1Ck2aNIGOjg7Mzc0xatQoPltLRERERESfTbFGNNevX/+54vhXPH/+HAcPHsSsWbOURmEBiM9dqqmpYenSpahevTru3r0Lf39/TJgwAStWrBDrZmZmYtasWdiwYQO0tLTg7++Pnj17Ijo6utD+jx07BlNTUxw7dgy3b99Gjx49UKdOHQwaNAgVKlSAmpoatm/fjoCAAKirq3/0eh48eICuXbti+PDhGDx4MC5cuICxY8cq1Llz5w68vLwwc+ZMrFu3Dv/73/8wYsQIjBgxosDvZ1ZWFrKyssT99PT0j8ZBRERERET0rk9aDOhrc/v2bQiCADs7uw/WCwgIgIeHBywsLNC8eXPMnDkTW7duVaiTnZ2N5cuXw9XVFfXr10dERAROnz6NmJiYQts1NDTE8uXLYWdnh/bt26Ndu3bic5xmZmZYunQppk2bBkNDQzRv3hw//vgj7t69W2h7oaGhqFGjBhYuXAhbW1v4+PjA19dXoc6cOXPg4+ODgIAAWFtbo3Hjxli6dCk2bNiA169fK7U5Z84chdFpc3PzD94rIiIiIiKi95WpRLOoq+T+8ccfaNGiBczMzKCrq4s+ffrg2bNnyMzMFOtoaGigQYMG4r6dnR0MDAwQHx9faLu1atVSGKk0NTXF06dPxf3hw4fj8ePH2LhxI1xdXbFt2zbUqlULR44cKbC9+Ph4NGzYUKHM1dVVYT8uLg7h4eHiM6NyuRyenp7Iy8tDUlKSUpuBgYFIS0sTt3enAxMRERERERVFmUo0ra2tIZFIPrjgT3JyMtq3bw9HR0dERkbi4sWLCAkJAQC8efPmk/rX1NRU2JdIJMjLy1Mo09XVRYcOHTBr1izExcWhSZMmmDlzZon7zMjIwJAhQxAbGytucXFxSExMRI0aNZTqS6VS6OnpKWxERERERETFUaYSTSMjI3h6eiIkJKTAxXBSU1Nx8eJF5OXlYeHChWjUqBFsbGzw6NEjpbo5OTm4cOGCuJ+QkIDU1FTY29urLF6JRAI7O7tCF+6xt7dXmqqbv6BRvnr16uHGjRuwsrJS2rS0tFQWKxERERERUb4ylWgCQEhICHJzc+Hi4oLIyEgkJiYiPj4eS5cuhaurK6ysrJCdnY1ly5bh7t27+OWXX7By5UqldjQ1NTFy5EicO3cOFy9ehK+vLxo1agQXF5cSxRUbG4tOnTph+/btuHHjBm7fvo2wsDCsW7cOnTp1KvCcoUOHIjExEePHj0dCQgJ+++03hIeHK9SZOHEiTp8+jREjRiA2NhaJiYnYtWsXRowYUaI4iYiIiIiIPqbMJZqWlpa4dOkSPDw8MHbsWNSuXRutWrXC0aNHERoaCicnJyxatAhz585F7dq1sXHjRsyZM0epnXLlymHixIno3bs33NzcIJfLsWXLlhLHVaVKFVhYWCA4OBgNGzZEvXr1sGTJEgQHB2Py5MkFnlO1alVERkbi999/h5OTE1auXInZs2cr1HF0dMTx48dx69YtNGnSBHXr1sW0adNQuXLlEsdKRERERET0IRKhqCvkUJmUnp4OfX19TD9xF9py3dIOh75Ak+qWL+0QiIiIiOhfkJ8bpKWlfXQtlzI3oklERERERESfl0ZpB0BfhzFOxlyBloiIiIiIioQjmkRERERERKRSTDSJiIiIiIhIpZhoEhERERERkUrxGU0qkkVxz6Atf1PaYdAXgKvMEhEREdHHcESTiIiIiIiIVIqJJhEREREREakUE00iIiIiIiJSqf9MohkVFQWJRILU1NTSDoWIiIiIiKhM++oSzTNnzkBdXR3t2rUr7VCK7PLly+jRowdMTU0hlUpRrVo1tG/fHnv27IEgCCrpIygoCHXq1FFJW0RERERERJ/iq0s0w8LCMHLkSJw4cQKPHj0q7XA+ateuXWjUqBEyMjIQERGB+Ph4HDx4EF26dMGUKVOQlpZW2iESERERERGp1FeVaGZkZGDLli0YNmwY2rVrh/Dw8A/WX7NmDczNzVGuXDl06dIFixYtgoGBgXjc19cXnTt3VjgnICAA7u7u4r67uztGjRqFCRMmwMjICCYmJggKCipSvK9evYKfnx/atWuHffv2oXXr1rC0tIS9vT38/PwQFxcHfX19AEBubi78/PxQvXp16OjowNbWFkuWLFFoLyoqCi4uLpDJZDAwMICbmxvu3buH8PBwBAcHIy4uDhKJBBKJRLw39+/fR6dOnSCXy6Gnpwdvb288efKkSPETERERERGVxFeVaG7duhV2dnawtbXFd999h3Xr1hU69TQ6OhpDhw7F6NGjERsbi1atWmHWrFkl6jciIgIymQznzp3DvHnzMGPGDBw5cuSj5x0+fBjPnj3DhAkTCq0jkUgAAHl5eahSpQq2bduGGzduYNq0afjhhx+wdetWAEBOTg46d+6MZs2a4cqVKzhz5gwGDx4MiUSCHj16YOzYsahVqxZSUlKQkpKCHj16IC8vD506dcLz589x/PhxHDlyBHfv3kWPHj0KjScrKwvp6ekKGxERERERUXFolHYAxREWFobvvvsOAODl5YW0tDQcP35cYQQy37Jly9CmTRuMGzcOAGBjY4PTp09j7969xe7X0dER06dPBwBYW1tj+fLlOHr0KFq1avXB827dugUAsLW1FcvOnz8PDw8PcX/z5s1o3749NDU1ERwcLJZXr14dZ86cwdatW+Ht7Y309HSkpaWhffv2qFGjBgDA3t5erC+Xy6GhoQETExOx7MiRI7h69SqSkpJgbm4OANiwYQNq1aqF8+fPo0GDBkoxz5kzRyEOIiIiIiKi4vpqRjQTEhIQExODXr16AQA0NDTQo0cPhIWFFVrfxcVFoez9/aJydHRU2Dc1NcXTp09L3FZsbCxiY2Px6tUr5OTkiMdCQkJQv359VKhQAXK5HKtXr8b9+/cBAEZGRvD19YWnpyc6dOiAJUuWICUl5YN9xcfHw9zcXEwyAaBmzZowMDBAfHx8gecEBgYiLS1N3B48eFCi6yQiIiIiorLrq0k0w8LCkJOTg8qVK0NDQwMaGhoIDQ1FZGRkiRfUUVNTU5p6m52drVRPU1NTYV8ikSAvL++j7VtbWwN4m/Tmk0qlsLKygpWVlULdzZs3Y9y4cfDz88Phw4cRGxuL/v37482bN2Kd9evX48yZM2jcuDG2bNkCGxsbnD179uMXWgxSqRR6enoKGxERERERUXF8FYlmTk4ONmzYgIULF4qjgbGxsYiLi0PlypWxadMmpXNsbW1x/vx5hbL39ytUqKA0KhgbG6uyuFu3bg0jIyPMnTv3o3Wjo6PRuHFj+Pv7o27durCyssKdO3eU6tWtWxeBgYE4ffo0ateujd9++w0AoKWlhdzcXIW69vb2ePDggcKo5I0bN5CamoqaNWt+4tUREREREREV7KtINPfu3YsXL17Az88PtWvXVti6detW4PTZkSNHYv/+/Vi0aBESExOxatUqHDhwQFx8BwCaN2+OCxcuYMOGDUhMTMT06dNx7do1lcUtl8uxdu1a7Nu3D+3atcOhQ4dw9+5dXLlyBfPmzQMAqKurA3g7+nnhwgUcOnQIt27dwtSpUxUS46SkJAQGBuLMmTO4d+8eDh8+jMTERPE5TQsLCyQlJSE2NhZ///03srKy0LJlSzg4OMDHxweXLl1CTEwM+vbti2bNmsHZ2Vll10lERERERPSuryLRDAsLQ8uWLcVXgbyrW7duuHDhAq5cuaJQ7ubmhpUrV2LRokVwcnLCwYMH8f3330NbW1us4+npialTp2LChAlo0KABXr58ib59+6o09i5duuD06dMoV64c+vbtC1tbWzRv3hx//vmnuBAQAAwZMgRdu3ZFjx490LBhQzx79gz+/v5iO+XKlcPNmzfRrVs32NjYYPDgwRg+fDiGDBki3gcvLy94eHigQoUK2LRpEyQSCXbt2gVDQ0M0bdoULVu2hKWlJbZs2aLSayQiIiIiInqXRCjs/SD/QYMGDcLNmzdx8uTJ0g7lq5Geng59fX1MP3EX2nLd0g6HvgCT6pYv7RCIiIiIqBTk5wZpaWkfXcvlq3q9SXEtWLAArVq1gkwmw4EDBxAREYEVK1aUdlhERERERET/af/pRDMmJgbz5s3Dy5cvYWlpiaVLl2LgwIEqa3/jxo3i1NX3VatWDdevX1dZX6VtjJMxV6AlIiIiIqIiKVNTZ1Xt5cuXePLkSYHHNDU1Ua1atX85ItUrzvA4ERERERH9d3Hq7L9EV1cXurp8bpGIiIiIiOhdX8Wqs0RERERERPT14IgmFcmiuGfQlr8p7TDKJK7ySkRERERfG45oEhERERERkUox0SQiIiIiIiKVYqJJREREREREKsVE8yvk6+uLzp07l3YYREREREREBSpziebjx48xevRoWFlZQVtbG5UqVYKbmxtCQ0ORmZlZ2uEBANasWQMnJyfI5XIYGBigbt26mDNnjnh8yZIlCA8PL70AiYiIiIiIPqBMrTp79+5duLm5wcDAALNnz4aDgwOkUimuXr2K1atXw8zMDB07dlQ6Lzs7G5qamv9KjOvWrUNAQACWLl2KZs2aISsrC1euXMG1a9fEOvr6+v9KLERERERERCVRpkY0/f39oaGhgQsXLsDb2xv29vawtLREp06dsG/fPnTo0AEAIJFIEBoaio4dO0Imk2HWrFkAgNDQUNSoUQNaWlqwtbXFL7/8IradnJwMiUSC2NhYsSw1NRUSiQRRUVEAgKioKEgkEuzbtw+Ojo7Q1tZGo0aNFJLI3bt3w9vbG35+frCyskKtWrXQq1cvMQZAcepsfr/vb+7u7mL9U6dOoUmTJtDR0YG5uTlGjRqFV69eqfjuEhERERERvVVmEs1nz57h8OHDGD58OGQyWYF1JBKJ+HVQUBC6dOmCq1evYsCAAdi5cydGjx6NsWPH4tq1axgyZAj69++PY8eOFTuW8ePHY+HChTh//jwqVKiADh06IDs7GwBgYmKCs2fP4t69e0Vqy9zcHCkpKeJ2+fJlGBsbo2nTpgCAO3fuwMvLC926dcOVK1ewZcsWnDp1CiNGjCiwvaysLKSnpytsRERERERExVFmEs3bt29DEATY2toqlJcvXx5yuRxyuRwTJ04Uy3v37o3+/fvD0tISVatWxYIFC+Dr6wt/f3/Y2NhgzJgx6Nq1KxYsWFDsWKZPn45WrVrBwcEBERERePLkCXbu3CkeMzAwgIWFBWxtbeHr64utW7ciLy+vwLbU1dVhYmICExMTGBgYYOjQoXB1dUVQUBAAYM6cOfDx8UFAQACsra3RuHFjLF26FBs2bMDr16+V2pszZw709fXFzdzcvNjXR0REREREZVuZSTQLExMTg9jYWNSqVQtZWVliubOzs0K9+Ph4uLm5KZS5ubkhPj6+2H26urqKXxsZGcHW1lZsx9TUFGfOnMHVq1cxevRo5OTkoF+/fvDy8io02cw3YMAAvHz5Er/99hvU1N5+a+Pi4hAeHi4m03K5HJ6ensjLy0NSUpJSG4GBgUhLSxO3Bw8eFPv6iIiIiIiobCsziwFZWVlBIpEgISFBodzS0hIAoKOjo1Be2PTawuQndoIgiGX502FLonbt2qhduzb8/f0xdOhQNGnSBMePH4eHh0eB9WfOnIlDhw4hJiYGurq6YnlGRgaGDBmCUaNGKZ1TtWpVpTKpVAqpVFriuImIiIiIiMrMiKaxsTFatWqF5cuXl2ghHHt7e0RHRyuURUdHo2bNmgCAChUqAABSUlLE4+8uDPSus2fPil+/ePECt27dgr29faF95/dRWNyRkZGYMWMGtm7diho1aigcq1evHm7cuAErKyulTUtLq9A+iYiIiIiISqrMjGgCwIoVK+Dm5gZnZ2cEBQXB0dERampqOH/+PG7evIn69esXeu748ePh7e2NunXromXLltizZw927NiBP/74A8DbEdFGjRrhp59+QvXq1fH06VNMmTKlwLZmzJgBY2NjVKpUCZMnT0b58uXFVWSHDRuGypUro3nz5qhSpQpSUlIwc+ZMVKhQQWHKbb5r166hb9++mDhxImrVqoXHjx8DALS0tGBkZISJEyeiUaNGGDFiBAYOHAiZTIYbN27gyJEjWL58+SfeUSIiIiIiImVlZkQTAGrUqIHLly+jZcuWCAwMhJOTE5ydnbFs2TKMGzcOP/74Y6Hndu7cGUuWLMGCBQtQq1YtrFq1CuvXr1d4jci6deuQk5OD+vXrIyAgADNnziywrZ9++gmjR49G/fr18fjxY+zZs0ccXWzZsiXOnj2L7t27w8bGBt26dYO2tjaOHj0KY2NjpbYuXLiAzMxMzJw5E6ampuLWtWtXAICjoyOOHz+OW7duoUmTJqhbty6mTZuGypUrf8KdJCIiIiIiKpxEePehQvqsoqKi4OHhgRcvXsDAwKC0wymS9PR06OvrY/qJu9CW6378BFK5SXXLl3YIRERERERibpCWlgY9Pb0P1i1TI5pERERERET0+ZWpZzSp5MY4GX/0rxZEREREREQAE81/lbu7OzhTmYiIiIiI/us4dZaIiIiIiIhUiokmERERERERqRQTTSIiIiIiIlIpPqNJRbIo7hm05W9KO4yvCl9LQkRERERlFUc0iYiIiIiISKWYaBIREREREZFKMdH8RBYWFli8ePFn7yc5ORkSiQSxsbGfvS8iIiIiIqJP8Z9INH19fSGRSCCRSKCpqYlKlSqhVatWWLduHfLy8lTSR3h4OAwMDJTKz58/j8GDB6ukj3y+vr7o3LmzQpm5uTlSUlJQu3ZtlfZFRERERESkav+JRBMAvLy8kJKSguTkZBw4cAAeHh4YPXo02rdvj5ycnM/Wb4UKFVCuXLnP1n4+dXV1mJiYQEOD6zcREREREdGX7T+TaEqlUpiYmMDMzAz16tXDDz/8gF27duHAgQMIDw8HAKSmpmLgwIGoUKEC9PT00Lx5c8TFxYltxMXFwcPDA7q6utDT00P9+vVx4cIFREVFoX///khLSxNHToOCggAoT52VSCRYu3YtunTpgnLlysHa2hq7d+8Wj+fm5sLPzw/Vq1eHjo4ObG1tsWTJEvF4UFAQIiIisGvXLrGvqKioAqfOHj9+HC4uLpBKpTA1NcWkSZMUkmp3d3eMGjUKEyZMgJGREUxMTMS4iYiIiIiIPpf/TKJZkObNm8PJyQk7duwAAHTv3h1Pnz7FgQMHcPHiRdSrVw8tWrTA8+fPAQA+Pj6oUqUKzp8/j4sXL2LSpEnQ1NRE48aNsXjxYujp6SElJQUpKSkYN25cof0GBwfD29sbV65cQdu2beHj4yP2kZeXhypVqmDbtm24ceMGpk2bhh9++AFbt24FAIwbNw7e3t7iCG1KSgoaN26s1Mdff/2Ftm3bokGDBoiLi0NoaCjCwsIwc+ZMhXoRERGQyWQ4d+4c5s2bhxkzZuDIkSOFxp6VlYX09HSFjYiIiIiIqDj+8/Mw7ezscOXKFZw6dQoxMTF4+vQppFIpAGDBggX4/fffsX37dgwePBj379/H+PHjYWdnBwCwtrYW29HX14dEIoGJiclH+/T19UWvXr0AALNnz8bSpUsRExMDLy8vaGpqIjg4WKxbvXp1nDlzBlu3boW3tzfkcjl0dHSQlZX1wb5WrFgBc3NzLF++HBKJBHZ2dnj06BEmTpyIadOmQU3t7d8QHB0dMX36dPF6li9fjqNHj6JVq1YFtjtnzhyF+IiIiIiIiIrrPz2iCQCCIEAikSAuLg4ZGRkwNjaGXC4Xt6SkJNy5cwcAMGbMGAwcOBAtW7bETz/9JJYXl6Ojo/i1TCaDnp4enj59KpaFhISgfv36qFChAuRyOVavXo379+8Xq4/4+Hi4urpCIpGIZW5ubsjIyMDDhw8LjAUATE1NFWJ5X2BgINLS0sTtwYMHxYqLiIiIiIjoPz+iGR8fj+rVqyMjIwOmpqaIiopSqpO/mmxQUBB69+6Nffv24cCBA5g+fTo2b96MLl26FKtPTU1NhX2JRCKufrt582aMGzcOCxcuhKurK3R1dTF//nycO3euRNf3KbEURCqViiO+REREREREJfGfTjT//PNPXL16Fd9//z2qVKmCx48fQ0NDAxYWFoWeY2NjAxsbG3z//ffo1asX1q9fjy5dukBLSwu5ubmfHFN0dDQaN24Mf39/sez9kdOi9GVvb4/IyEhxxDa/bV1dXVSpUuWT4yQiIiIiov/X3p3HRVX3//9/jiADggxXqCiJYooIJoo7chlWGqS5lFeomXtmJi6Xy83IXHJJLcxc0hYNtFup9WnRsiwzpUTNFdEkJdO0UkkTEO1ShPP7o5/zdQIXbMZhedxvt3NrOOd93uf1nveFXk/fZ87gVpWZW2cvXryokydP6tdff9Xu3bv1wgsvqGvXrnrooYfUt29ftW/fXhEREerWrZu+/PJLHT16VFu2bNGECRO0c+dO/fnnn4qLi9OmTZv0888/KyUlRTt27FBISIikv54um5ubqw0bNuj06dO6cOHCLdUZFBSknTt36osvvtChQ4c0ceJE7dixw6ZNYGCg0tLSdPDgQZ0+fVp5eXmF+nn66ad1/PhxDR8+XD/88INWr16tyZMna/To0dbPZwIAAACAM5SZRLJu3TrVqFFDgYGBiomJ0caNGzV//nytXr1aLi4uMplM+uyzz3TPPfdowIABql+/vnr27Kmff/5Zfn5+cnFx0ZkzZ9S3b1/Vr19fsbGxevDBB60PxmnTpo2eeuop9ejRQ1WrVtWLL754S3UOGTJEjzzyiHr06KFWrVrpzJkzNqubkjR48GAFBwerefPmqlq1qlJSUgr1c+edd+qzzz7T9u3b1bhxYz311FMaNGiQnnvuuVuqCwAAAADsxWQYhuHsIlBy5eTkyGKxaPI3P8ndq7KzyylVngmv4uwSAAAAALu5kg2ys7Pl7e193bZlZkUTAAAAAFAyEDQBAAAAAHZVpp86C/sZ3dj3hsvjAAAAACCxogkAAAAAsDOCJgAAAADArgiaAAAAAAC74jOauCkv7z0jd69Lzi6jROBrSwAAAIDrY0UTAAAAAGBXBE0AAAAAgF0RNAEAAAAAdkXQdIIpU6aoSZMmzi4DAAAAAByiVAbN/v37q1u3boX2b9q0SSaTSVlZWbe9pqu1a9dOJpNJJpNJ7u7uCg0N1aJFi5xa05W6Ro0a5ewyAAAAAJRxpTJolgaDBw/WiRMndODAAcXGxmrYsGFasWKFs8sCAAAAAIcr00Hzgw8+UMOGDWU2mxUYGKg5c+bYHA8MDNT06dPVt29feXl5qXbt2lqzZo1+//13de3aVV5eXgoLC9POnTttztu8ebPatm0rDw8PBQQEaMSIETp//rxNm0qVKql69eq66667NGXKFAUFBWnNmjVF1rljxw516NBBVapUkcViUVRUlHbv3m3TxmQyacmSJXr44YdVqVKlIvvbv3+/HnzwQXl5ecnPz099+vTR6dOnJf21CpycnKx58+ZZV1uPHj1aqJaLFy8qJyfHZgMAAACA4iizQXPXrl2KjY1Vz549tW/fPk2ZMkUTJ05UUlKSTbu5c+cqMjJSe/bsUadOndSnTx/17dtXjz/+uHbv3q26deuqb9++MgxDknT48GHFxMSoe/fuSktL06pVq7R582bFxcVdtx4PDw9dulT091CeO3dO/fr10+bNm7Vt2zYFBQWpY8eOOnfunE27559/XrGxsUpLS1PHjh3Vu3dv/fHHH5KkrKws3XfffQoPD9fOnTu1bt06nTp1SrGxsZKkefPmKSIiwrrSeuLECQUEBBSqZebMmbJYLNatqDYAAAAAcD2uzi7gVn366afy8vKy2Zefn299/fLLL+v+++/XxIkTJUn169fXgQMH9NJLL6l///7Wdh07dtSQIUMkSZMmTdLixYvVokULPfroo5Kk8ePHKyIiQqdOnVL16tU1c+ZM9e7d2/pZx6CgIM2fP19RUVFavHix3N3dC9W0YsUKpaWl6cknnyxyLPfdd5/Nz2+88YZ8fHyUnJyshx56yLq/f//+6tWrlyTphRde0Pz587V9+3bFxMRo4cKFCg8P1wsvvGBt/9ZbbykgIECHDh1S/fr15ebmZl1pvZb4+HiNHj3a+nNOTg5hEwAAAECxlNoVzXvvvVepqak225IlS6zH09PTFRkZaXNOZGSkMjIybAJpWFiY9bWfn58kqVGjRoX2ZWZmSpL27t2rpKQkeXl5Wbfo6GgVFBToyJEj1vMWLVokLy8veXh4aPDgwfrvf/+roUOHFjmWU6dOafDgwQoKCpLFYpG3t7dyc3N17Ngxm3ZX1+rp6Slvb2+bujZu3GhTV4MGDST9tQp7s8xms7y9vW02AAAAACiOUrui6enpqXr16tns++WXX4rdT8WKFa2vTSbTNfcVFBRIknJzczVkyBCNGDGiUF+1atWyvu7du7cmTJggDw8P1ahRQxUqXDvT9+vXT2fOnNG8efNUu3Ztmc1mRUREFLrV9uq6rtR2dV2dO3fW7NmzC/Vfo0aNa14bAAAAAOyt1AbNGwkJCVFKSorNvpSUFNWvX18uLi633G/Tpk114MCBQiH37ywWyw3bXF3XokWL1LFjR0nS8ePHrQ/xKU5dH3zwgQIDA+XqWvS0urm52azmAgAAAIAjlNpbZ29kzJgx2rBhg6ZNm6ZDhw5p2bJlWrhwocaOHfuP+h0/fry2bNmiuLg4paamKiMjQ6tXr77hw4CuJygoSG+//bbS09P13XffqXfv3vLw8ChWH8OGDdMff/yhXr16aceOHTp8+LC++OILDRgwwBouAwMD9d133+no0aM6ffq0dTUUAAAAAOypzAbNpk2b6r333tPKlSt19913a9KkSZo6darNg4BuRVhYmJKTk3Xo0CG1bdtW4eHhmjRpkvz9/W+5z6VLl+rs2bNq2rSp+vTpoxEjRqhatWrF6sPf318pKSnKz8/XAw88oEaNGmnUqFHy8fGx3rY7duxYubi4KDQ0VFWrVi30GVAAAAAAsAeTceV7O4Ai5OTkyGKxaPI3P8ndq7KzyykRngmv4uwSAAAAgNvuSjbIzs6+4UNDy+yKJgAAAADAOcrsw4BgX6Mb+/JVJwAAAABuCiuaAAAAAAC7ImgCAAAAAOyKoAkAAAAAsCs+o4mb8vLeM3L3uuTsMm6IJ8ICAAAAzseKJgAAAADArgiaAAAAAAC7ImgCAAAAAOyKoFkKtGvXTqNGjXJ2GQAAAABwU5weNE+ePKmRI0eqXr16cnd3l5+fnyIjI7V48WJduHDB2eVp06ZNMplMatiwofLz822O+fj4KCkpyTmFAQAAAEAJ5dSnzv7000+KjIyUj4+PXnjhBTVq1Ehms1n79u3TG2+8oTvvvFNdunQpdF5eXp4qVqx422tdvny5BgwYcFuv6yj5+fkymUyqUMHp/9YAAAAAoIxxasp4+umn5erqqp07dyo2NlYhISG666671LVrV61du1adO3eWJJlMJi1evFhdunSRp6enZsyYIUlavHix6tatKzc3NwUHB+vtt9+29n306FGZTCalpqZa92VlZclkMmnTpk2S/t9q5dq1axUWFiZ3d3e1bt1a+/fvL1Tr8OHDNXnyZF28eLHIsRTnel988YXCw8Pl4eGh++67T5mZmfr8888VEhIib29vPfbYY4VWcy9fvqy4uDhZLBZVqVJFEydOlGEY1uMXL17U2LFjdeedd8rT01OtWrWyXleSkpKS5OPjozVr1ig0NFRms1nHjh274RwBAAAAQHE5LWieOXNGX375pYYNGyZPT88i25hMJuvrKVOm6OGHH9a+ffs0cOBAffTRRxo5cqTGjBmj/fv3a8iQIRowYIA2btxY7FrGjRunOXPmaMeOHapatao6d+6svLw8mzajRo3S5cuXtWDBgmL3/3dTpkzRwoULtWXLFh0/flyxsbF65ZVX9O6772rt2rX68ssvC11n2bJlcnV11fbt2zVv3jy9/PLLWrJkifV4XFyctm7dqpUrVyotLU2PPvqoYmJilJGRYW1z4cIFzZ49W0uWLNH333+vatWqFart4sWLysnJsdkAAAAAoDicFjR//PFHGYah4OBgm/1VqlSRl5eXvLy8NH78eOv+xx57TAMGDNBdd92lWrVqKSEhQf3799fTTz+t+vXra/To0XrkkUeUkJBQ7FomT56sDh06qFGjRlq2bJlOnTqljz76yKZNpUqVNHnyZM2cOVPZ2dm3Nuj/3/Tp0xUZGanw8HANGjRIycnJWrx4scLDw9W2bVv95z//KRSYAwICNHfuXAUHB6t3794aPny45s6dK0k6duyYEhMT9f7776tt27aqW7euxo4dq3//+99KTEy09pGXl6dFixapTZs2Cg4OVqVKlQrVNnPmTFksFusWEBDwj8YKAAAAoPwpcR/Q2759u1JTU9WwYUOb21SbN29u0y49PV2RkZE2+yIjI5Wenl7sa0ZERFhf33HHHQoODi6yn0GDBsnX11ezZ88u9jWuFhYWZn3t5+enSpUq6a677rLZl5mZaXNO69atbVZ4IyIilJGRofz8fO3bt0/5+fmqX7++NaR7eXkpOTlZhw8ftp7j5uZmc+2ixMfHKzs727odP378H40VAAAAQPnjtIcB1atXTyaTSQcPHrTZfyVweXh42Oy/1u2113LlITdXf47x77fDFperq6tmzJih/v37Ky4u7pavd/WDjEwmU6EHG5lMJhUUFNx0Xbm5uXJxcdGuXbvk4uJic8zLy8v62sPDwyasFsVsNstsNt/0tQEAAADg75y2ounr66sOHTpo4cKFOn/+fLHPDwkJUUpKis2+lJQUhYaGSpKqVq0qSTpx4oT1+NUP6rnatm3brK/Pnj2rQ4cOKSQkpMi2jz76qBo2bKjnn3/eZn9xrncrvvvuO5uft23bpqCgILm4uCg8PFz5+fnKzMxUvXr1bLbq1avbrQYAAAAAuBlO/XqTRYsWKTIyUs2bN9eUKVMUFhamChUqaMeOHfrhhx/UrFmza547btw4xcbGKjw8XO3bt9cnn3yiDz/8UF999ZWkv1bvWrdurVmzZqlOnTrKzMzUc889V2RfU6dOla+vr/z8/DRhwgRVqVJF3bp1u+a1Z82apejoaJt9xbnerTh27JhGjx6tIUOGaPfu3VqwYIHmzJkjSapfv7569+6tvn37as6cOQoPD9fvv/+uDRs2KCwsTJ06dbJbHQAAAABwI079jGbdunW1Z88etW/fXvHx8WrcuLGaN2+uBQsWaOzYsZo2bdo1z+3WrZvmzZunhIQENWzYUK+//roSExPVrl07a5u33npLly9fVrNmzTRq1ChNnz69yL5mzZqlkSNHqlmzZjp58qQ++eQTubm5XfPa9913n+677z5dvnzZZv/NXu9W9O3bV3/++adatmypYcOGaeTIkXryySetxxMTE9W3b1+NGTNGwcHB6tatm3bs2KFatWrZrQYAAAAAuBkm4+oPFZYzmzZt0r333quzZ8/Kx8fH2eWUSDk5ObJYLJr8zU9y96rs7HJu6JnwKs4uAQAAACiTrmSD7OxseXt7X7dtiXvqLAAAAACgdHPqZzRReoxu7HvDf7UAAAAAAKmcB8127dqpHN85DAAAAAAOwa2zAAAAAAC7ImgCAAAAAOyqXN86i5v38t4zcve6dMN2PPUVAAAAACuaAAAAAAC7ImgCAAAAAOyKoAkAAAAAsCuCJgAAAADArspt0Ny6datcXFzUqVMnh13jxx9/1MCBA1WrVi2ZzWbdeeeduv/++/XOO+/o8uXLDrsuAAAAADhTuQ2aS5cu1fDhw/XNN9/ot99+s3v/27dvV9OmTZWenq5XX31V+/fv16ZNm/TEE09o8eLF+v777695bl5ent3rAQAAAIDbpVwGzdzcXK1atUpDhw5Vp06dlJSUZHN8zZo1CgoKkru7u+69914tW7ZMJpNJWVlZ1jabN29W27Zt5eHhoYCAAI0YMULnz5+XJBmGof79+6t+/fpKSUlR586dFRQUpKCgIPXq1UubN29WWFiYJOno0aMymUxatWqVoqKi5O7urnfeeUcFBQWaOnWqatasKbPZrCZNmmjdunXW62/atKlQTampqTKZTDp69KgkKSkpST4+Pvr444+t44mOjtbx48cd8r4CAAAAgFROg+Z7772nBg0aKDg4WI8//rjeeustGYYhSTpy5Ij+85//qFu3btq7d6+GDBmiCRMm2Jx/+PBhxcTEqHv37kpLS9OqVau0efNmxcXFSfor8KWnp2vs2LGqUKHot9hkMtn8/Mwzz2jkyJFKT09XdHS05s2bpzlz5ighIUFpaWmKjo5Wly5dlJGRUayxXrhwQTNmzNDy5cuVkpKirKws9ezZ85rtL168qJycHJsNAAAAAIqjXAbNpUuX6vHHH5ckxcTEKDs7W8nJyZKk119/XcHBwXrppZcUHBysnj17qn///jbnz5w5U71799aoUaMUFBSkNm3aaP78+Vq+fLn+97//6dChQ5Kk4OBg6zmZmZny8vKybosWLbLpc9SoUXrkkUdUp04d1ahRQwkJCRo/frx69uyp4OBgzZ49W02aNNErr7xSrLHm5eVp4cKFioiIULNmzbRs2TJt2bJF27dvL7L9zJkzZbFYrFtAQECxrgcAAAAA5S5oHjx4UNu3b1evXr0kSa6ururRo4eWLl1qPd6iRQubc1q2bGnz8969e5WUlGQTHKOjo1VQUKAjR44UeV1fX1+lpqYqNTVVPj4+unTpks3x5s2bW1/n5OTot99+U2RkpE2byMhIpaenF2u8rq6uNuNp0KCBfHx8rtlPfHy8srOzrRu32QIAAAAoLldnF3C7LV26VJcvX5a/v791n2EYMpvNWrhw4U31kZubqyFDhmjEiBGFjtWqVUt//vmnpL9Ca3h4uCTJxcVF9erVk/RX+Ps7T0/PYo3jyi25V275lezzECGz2Syz2fyP+wEAAABQfpWrFc3Lly9r+fLlmjNnjnV1MTU1VXv37pW/v79WrFih4OBg7dy50+a8HTt22PzctGlTHThwQPXq1Su0ubm5KTw8XA0aNFBCQoIKCgqKXae3t7f8/f2VkpJisz8lJUWhoaGSpKpVq0qSTpw4YT2emppa5JivHs/BgweVlZWlkJCQYtcFAAAAADejXK1ofvrppzp79qwGDRoki8Vic6x79+5aunSp3nvvPb388ssaP368Bg0apNTUVOtTaa88wGf8+PFq3bq14uLi9MQTT8jT01MHDhzQ+vXrtXDhQplMJiUmJqpDhw6KjIxUfHy8QkJClJeXp2+++Ua///67XFxcrlvruHHjNHnyZNWtW1dNmjRRYmKiUlNT9c4770iS6tWrp4CAAE2ZMkUzZszQoUOHNGfOnEL9VKxYUcOHD9f8+fPl6uqquLg4tW7dutDtwAAAAABgL+VqRXPp0qVq3759oZAp/RU0d+7cqXPnzun//u//9OGHHyosLEyLFy+2PnX2yi2lYWFhSk5O1qFDh9S2bVuFh4dr0qRJNrfjtm7dWrt27VJwcLCGDRum0NBQtWnTRitWrNDcuXM1dOjQ69Y6YsQIjR49WmPGjFGjRo20bt0669euSH8FyBUrVuiHH35QWFiYZs+erenTpxfqp1KlSho/frwee+wxRUZGysvLS6tWrbrl9xAAAAAAbsRkXP0hPxRpxowZeu2110rdg3GSkpI0atQom+/aLK6cnBxZLBZN/uYnuXtVvmH7Z8Kr3PK1AAAAAJRcV7JBdna2vL29r9u2XN06e7MWLVqkFi1ayNfXVykpKXrppZes35EJAAAAALg+gmYRMjIyNH36dP3xxx+qVauWxowZo/j4eGeX5VSjG/ve8F8tAAAAAEDi1lncQHGWxwEAAACUXcXJBuXqYUAAAAAAAMcjaAIAAAAA7IqgCQAAAACwK4ImAAAAAMCuCJoAAAAAALsiaAIAAAAA7IqgWcZMmTJFTZo0cXYZAAAAAMoxgmYJ8/vvv2vo0KGqVauWzGazqlevrujoaKWkpDi7NAAAAAC4Ka7OLgC2unfvrkuXLmnZsmW66667dOrUKW3YsEFnzpxxdmkAAAAAcFNY0SxBsrKy9O2332r27Nm69957Vbt2bbVs2VLx8fHq0qWLJOnYsWPq2rWrvLy85O3trdjYWJ06deqafRYUFGjq1KmqWbOmzGazmjRponXr1t2uIQEAAAAohwiaJYiXl5e8vLz08ccf6+LFi4WOFxQUqGvXrvrjjz+UnJys9evX66efflKPHj2u2ee8efM0Z84cJSQkKC0tTdHR0erSpYsyMjKKbH/x4kXl5OTYbAAAAABQHATNEsTV1VVJSUlatmyZfHx8FBkZqWeffVZpaWmSpA0bNmjfvn1699131axZM7Vq1UrLly9XcnKyduzYUWSfCQkJGj9+vHr27Kng4GDNnj1bTZo00SuvvFJk+5kzZ8pisVi3gIAARw0XAAAAQBlF0Cxhunfvrt9++01r1qxRTEyMNm3apKZNmyopKUnp6ekKCAiwCX+hoaHy8fFRenp6ob5ycnL022+/KTIy0mZ/ZGRkke0lKT4+XtnZ2dbt+PHj9h0gAAAAgDKPoFkCubu7q0OHDpo4caK2bNmi/v37a/Lkybfl2mazWd7e3jYbAAAAABQHQbMUCA0N1fnz5xUSEqLjx4/brDIeOHBAWVlZCg0NLXSet7e3/P39C301SkpKSpHtAQAAAMAe+HqTEuTMmTN69NFHNXDgQIWFhaly5crauXOnXnzxRXXt2lXt27dXo0aN1Lt3b73yyiu6fPmynn76aUVFRal58+ZF9jlu3DhNnjxZdevWVZMmTZSYmKjU1FS98847t3l0AAAAAMoLgmYJ4uXlpVatWmnu3Lk6fPiw8vLyFBAQoMGDB+vZZ5+VyWTS6tWrNXz4cN1zzz2qUKGCYmJitGDBgmv2OWLECGVnZ2vMmDHKzMxUaGio1qxZo6CgoNs4MgAAAADlickwDMPZRaDkysnJkcViUXZ2Np/XBAAAAMqx4mQDPqMJAAAAALArgiYAAAAAwK4ImgAAAAAAuyJoAgAAAADsiqAJAAAAALArgiYAAAAAwK4ImgAAAAAAuyJoAgAAAADsiqAJAAAAALArgiYAAAAAwK4ImqVE586dFRMTU+Sxb7/9ViaTSWlpaTKZTEpNTZUkHT16VCaTybr5+vrqgQce0J49e25j5QAAAADKG4JmKTFo0CCtX79ev/zyS6FjiYmJat68uby9vYs896uvvtKJEyf0xRdfKDc3Vw8++KCysrIcXDEAAACA8oqgWUo89NBDqlq1qpKSkmz25+bm6v3339egQYOuea6vr6+qV6+u5s2bKyEhQadOndJ3333n4IoBAAAAlFcEzVLC1dVVffv2VVJSkgzDsO5///33lZ+fr169et1UPx4eHpKkS5cuFXn84sWLysnJsdkAAAAAoDgImqXIwIEDdfjwYSUnJ1v3JSYmqnv37rJYLDc8PysrS9OmTZOXl5datmxZZJuZM2fKYrFYt4CAALvVDwAAAKB8IGiWIg0aNFCbNm301ltvSZJ+/PFHffvtt9e9bVaS2rRpIy8vL/3rX//S3r17tWrVKvn5+RXZNj4+XtnZ2dbt+PHjdh8HAAAAgLLN1dkFoHgGDRqk4cOH69VXX1ViYqLq1q2rqKio656zatUqhYaGytfXVz4+PtdtazabZTab7VgxAAAAgPKGFc1SJjY2VhUqVNC7776r5cuXa+DAgTKZTNc9JyAgQHXr1r1hyAQAAAAAe2BFs5Tx8vJSjx49FB8fr5ycHPXv39/ZJQEAAACADVY0S6FBgwbp7Nmzio6Olr+/v7PLAQAAAAAbJuPq78oA/iYnJ0cWi0XZ2dny9vZ2djkAAAAAnKQ42YAVTQAAAACAXRE0AQAAAAB2RdAEAAAAANgVQRMAAAAAYFcETQAAAACAXRE0AQAAAAB2RdAEAAAAANgVQRMAAAAAYFcETQAAAACAXRE0AQAAAAB2RdAEAAAAANgVQRMAAAAAYFcETQAAAACAXRE0AQAAAAB2RdAEAAAAANgVQRMAAAAAYFcETQAAAACAXRE0AQAAAAB2RdAEAAAAANgVQRMAAAAAYFcETQAAAACAXRE0AQAAAAB25ersAlCyGYYhScrJyXFyJQAAAACc6UomuJIRroegies6c+aMJCkgIMDJlQAAAAAoCc6dOyeLxXLdNgRNXNcdd9whSTp27NgN/8cEx8rJyVFAQICOHz8ub29vZ5dTbjEPJQPzUDIwDyUD81AyMA8lA/PgWIZh6Ny5c/L3979hW4ImrqtChb8+xmuxWPhlLSG8vb2ZixKAeSgZmIeSgXkoGZiHkoF5KBmYB8e52cUnHgYEAAAAALArgiYAAAAAwK4Imrgus9msyZMny2w2O7uUco+5KBmYh5KBeSgZmIeSgXkoGZiHkoF5KDlMxs08mxYAAAAAgJvEiiYAAAAAwK4ImgAAAAAAuyJoAgAAAADsiqAJAAAAALArgmY59OqrryowMFDu7u5q1aqVtm/fft3277//vho0aCB3d3c1atRIn332mc1xwzA0adIk1ahRQx4eHmrfvr0yMjIcOYQywZ7zkJeXp/Hjx6tRo0by9PSUv7+/+vbtq99++83Rwyj17P37cLWnnnpKJpNJr7zyip2rLnscMQ/p6enq0qWLLBaLPD091aJFCx07dsxRQygT7D0Pubm5iouLU82aNeXh4aHQ0FC99tprjhxCmVCcefj+++/VvXt3BQYGXvfPm+LOLf5i77mYOXOmWrRoocqVK6tatWrq1q2bDh486MARlA2O+J24YtasWTKZTBo1apR9i4ZkoFxZuXKl4ebmZrz11lvG999/bwwePNjw8fExTp06VWT7lJQUw8XFxXjxxReNAwcOGM8995xRsWJFY9++fdY2s2bNMiwWi/Hxxx8be/fuNbp06WLUqVPH+PPPP2/XsEode89DVlaW0b59e2PVqlXGDz/8YGzdutVo2bKl0axZs9s5rFLHEb8PV3z44YdG48aNDX9/f2Pu3LkOHknp5oh5+PHHH4077rjDGDdunLF7927jxx9/NFavXn3NPuGYeRg8eLBRt25dY+PGjcaRI0eM119/3XBxcTFWr159u4ZV6hR3HrZv326MHTvWWLFihVG9evUi/7wpbp/4iyPmIjo62khMTDT2799vpKamGh07djRq1apl5ObmOng0pZcj5uHqtoGBgUZYWJgxcuRIxwygHCNoljMtW7Y0hg0bZv05Pz/f8Pf3N2bOnFlk+9jYWKNTp042+1q1amUMGTLEMAzDKCgoMKpXr2689NJL1uNZWVmG2Ww2VqxY4YARlA32noeibN++3ZBk/Pzzz/Ypugxy1Dz88ssvxp133mns37/fqF27NkHzBhwxDz169DAef/xxxxRcRjliHho2bGhMnTrVpk3Tpk2NCRMm2LHysqW483C1a/1580/6LM8cMRd/l5mZaUgykpOT/0mpZZqj5uHcuXNGUFCQsX79eiMqKoqg6QDcOluOXLp0Sbt27VL79u2t+ypUqKD27dtr69atRZ6zdetWm/aSFB0dbW1/5MgRnTx50qaNxWJRq1atrtlneeeIeShKdna2TCaTfHx87FJ3WeOoeSgoKFCfPn00btw4NWzY0DHFlyGOmIeCggKtXbtW9evXV3R0tKpVq6ZWrVrp448/dtg4SjtH/T60adNGa9as0a+//irDMLRx40YdOnRIDzzwgGMGUsrdyjw4o8/y4Ha9b9nZ2ZKkO+64w259liWOnIdhw4apU6dOhf4cg/0QNMuR06dPKz8/X35+fjb7/fz8dPLkySLPOXny5HXbX/lvcfos7xwxD3/3v//9T+PHj1evXr3k7e1tn8LLGEfNw+zZs+Xq6qoRI0bYv+gyyBHzkJmZqdzcXM2aNUsxMTH68ssv9fDDD+uRRx5RcnKyYwZSyjnq92HBggUKDQ1VzZo15ebmppiYGL366qu655577D+IMuBW5sEZfZYHt+N9Kygo0KhRoxQZGam7777bLn2WNY6ah5UrV2r37t2aOXPmPy0R1+Hq7AIA2FdeXp5iY2NlGIYWL17s7HLKlV27dmnevHnavXu3TCaTs8sptwoKCiRJXbt21X//+19JUpMmTbRlyxa99tprioqKcmZ55cqCBQu0bds2rVmzRrVr19Y333yjYcOGyd/fn1UElHvDhg3T/v37tXnzZmeXUq4cP35cI0eO1Pr16+Xu7u7scso0VjTLkSpVqsjFxUWnTp2y2X/q1ClVr169yHOqV69+3fZX/lucPss7R8zDFVdC5s8//6z169ezmnkdjpiHb7/9VpmZmapVq5ZcXV3l6uqqn3/+WWPGjFFgYKBDxlHaOWIeqlSpIldXV4WGhtq0CQkJ4amz1+CIefjzzz/17LPP6uWXX1bnzp0VFhamuLg49ejRQwkJCY4ZSCl3K/PgjD7LA0e/b3Fxcfr000+1ceNG1axZ8x/3V1Y5Yh527dqlzMxMNW3a1Pp3dXJysubPny9XV1fl5+fbo3SIoFmuuLm5qVmzZtqwYYN1X0FBgTZs2KCIiIgiz4mIiLBpL0nr16+3tq9Tp46qV69u0yYnJ0fffffdNfss7xwxD9L/C5kZGRn66quv5Ovr65gBlBGOmIc+ffooLS1Nqamp1s3f31/jxo3TF1984bjBlGKOmAc3Nze1aNGi0FcGHDp0SLVr17bzCMoGR8xDXl6e8vLyVKGC7f/VcHFxsa46w9atzIMz+iwPHPW+GYahuLg4ffTRR/r6669Vp04de5RbZjliHu6//37t27fP5u/q5s2bq3fv3kpNTZWLi4u9yoeTH0aE22zlypWG2Ww2kpKSjAMHDhhPPvmk4ePjY5w8edIwDMPo06eP8cwzz1jbp6SkGK6urkZCQoKRnp5uTJ48ucivN/Hx8TFWr15tpKWlGV27duXrTW7A3vNw6dIlo0uXLkbNmjWN1NRU48SJE9bt4sWLThljaeCI34e/46mzN+aIefjwww+NihUrGm+88YaRkZFhLFiwwHBxcTG+/fbb2z6+0sIR8xAVFWU0bNjQ2Lhxo/HTTz8ZiYmJhru7u7Fo0aLbPr7SorjzcPHiRWPPnj3Gnj17jBo1ahhjx4419uzZY2RkZNx0nyiaI+Zi6NChhsViMTZt2mTzd/WFCxdu+/hKC0fMw9/x1FnHIGiWQwsWLDBq1apluLm5GS1btjS2bdtmPRYVFWX069fPpv17771n1K9f33BzczMaNmxorF271uZ4QUGBMXHiRMPPz88wm83G/fffbxw8ePB2DKVUs+c8HDlyxJBU5LZx48bbNKLSyd6/D39H0Lw5jpiHpUuXGvXq1TPc3d2Nxo0bGx9//LGjh1Hq2XseTpw4YfTv39/w9/c33N3djeDgYGPOnDlGQUHB7RhOqVWcebjWn/9RUVE33Seuzd5zca2/qxMTE2/foEohR/xOXI2g6RgmwzCM27R4CgAAAAAoB/iMJgAAAADArgiaAAAAAAC7ImgCAAAAAOyKoAkAAAAAsCuCJgAAAADArgiaAAAAAAC7ImgCAAAAAOyKoAkAAAAAsCuCJgAAAADArgiaAACUIP3791e3bt2cXUaRjh49KpPJpNTUVGeXAgAo4QiaAADghi5duuTsEgAApQhBEwCAEqpdu3YaPny4Ro0apX/961/y8/PTm2++qfPnz2vAgAGqXLmy6tWrp88//9x6zqZNm2QymbR27VqFhYXJ3d1drVu31v79+236/uCDD9SwYUOZzWYFBgZqzpw5NscDAwM1bdo09e3bV97e3nryySdVp04dSVJ4eLhMJpPatWsnSdqxY4c6dOigKlWqyGKxKCoqSrt377bpz2QyacmSJXr44YdVqVIlBQUFac2aNTZtvv/+ez300EPy9vZW5cqV1bZtWx0+fNh6fMmSJQoJCZG7u7saNGigRYsW/eP3GADgGARNAABKsGXLlqlKlSravn27hg8frqFDh+rRRx9VmzZttHv3bj3wwAPq06ePLly4YHPeuHHjNGfOHO3YsUNVq1ZV586dlZeXJ0natWuXYmNj1bNnT+3bt09TpkzRxIkTlZSUZNNHQkKCGjdurD179mjixInavn27JOmrr77SiRMn9OGHH0qSzp07p379+mnz5s3atm2bgoKC1LFjR507d86mv+eff16xsbFKS0tTx44d1bt3b/3xxx+SpF9//VX33HOPzGazvv76a+3atUsDBw7U5cuXJUnvvPOOJk2apBkzZig9PV0vvPCCJk6cqGXLltn9PQcA2IEBAABKjH79+hldu3Y1DMMwoqKijH//+9/WY5cvXzY8PT2NPn36WPedOHHCkGRs3brVMAzD2LhxoyHJWLlypbXNmTNnDA8PD2PVqlWGYRjGY489ZnTo0MHmuuPGjTNCQ0OtP9euXdvo1q2bTZsjR44Ykow9e/Zcdwz5+flG5cqVjU8++cS6T5Lx3HPPWX/Ozc01JBmff/65YRiGER8fb9SpU8e4dOlSkX3WrVvXePfdd232TZs2zYiIiLhuLQAA52BFEwCAEiwsLMz62sXFRb6+vmrUqJF1n5+fnyQpMzPT5ryIiAjr6zvuuEPBwcFKT0+XJKWnpysyMtKmfWRkpDIyMpSfn2/d17x585uq8dSpUxo8eLCCgoJksVjk7e2t3NxcHTt27Jpj8fT0lLe3t7Xu1NRUtW3bVhUrVizU//nz53X48GENGjRIXl5e1m369Ok2t9YCAEoOV2cXAAAAru3vwctkMtnsM5lMkqSCggK7X9vT0/Om2vXr109nzpzRvHnzVLt2bZnNZkVERBR6gFBRY7lSt4eHxzX7z83NlSS9+eabatWqlc0xFxeXm6oRAHB7ETQBACiDtm3bplq1akmSzp49q0OHDikkJESSFBISopSUFJv2KSkpql+//nWDm5ubmyTZrHpeOXfRokXq2LGjJOn48eM6ffp0seoNCwvTsmXLlJeXVyiQ+vn5yd/fXz/99JN69+5drH4BAM5B0AQAoAyaOnWqfH195efnpwkTJqhKlSrW7+ccM2aMWrRooWnTpqlHjx7aunWrFi5ceMOnuFarVk0eHh5at26datasKXd3d1ksFgUFBentt99W8+bNlZOTo3Hjxl13hbIocXFxWrBggXr27Kn4+HhZLBZt27ZNLVu2VHBwsJ5//nmNGDFCFotFMTExunjxonbu3KmzZ89q9OjRt/o2AQAchM9oAgBQBs2aNUsjR45Us2bNdPLkSX3yySfWFcmmTZvqvffe08qVK3X33Xdr0qRJmjp1qvr373/dPl1dXTV//ny9/vrr8vf3V9euXSVJS5cu1dmzZ9W0aVP16dNHI0aMULVq1YpVr6+vr77++mvl5uYqKipKzZo105tvvmld3XziiSe0ZMkSJSYmqlGjRoqKilJSUpL1K1cAACWLyTAMw9lFAAAA+9i0aZPuvfdenT17Vj4+Ps4uBwBQTrGiCQAAAACwK4ImAAAAAMCuuHUWAAAAAGBXrGgCAAAAAOyKoAkAAAAAsCuCJgAAAADArgiaAAAAAAC7ImgCAAAAAOyKoAkAAAAAsCuCJgAAAADArgiaAAAAAAC7+v8AfO/Jz3aDDEUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = final_model.feature_importances_\n",
    "\n",
    "# Crear un DataFrame para visualizarlo mejor (asumiendo que tienes los nombres de las características)\n",
    "feature_names = X.columns if isinstance(X, pd.DataFrame) else [f'Feature {i}' for i in range(X.shape[1])]\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Importance': feature_importance\n",
    "})\n",
    "\n",
    "# Ordenar de mayor a menor importancia\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Visualizar la importancia de las características\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'], color='skyblue')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Feature Importances in GradientBoostingClassifier')\n",
    "plt.gca().invert_yaxis()  # Para que las características más importantes estén en la parte superior\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parece que la variable de Solo no esta aportando nada al modelo, y se podría valorar también la exclusión de la variable VIP. Reentrenamos el modelo con esas condiciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_eliminadas = ['Solo', 'VIP']\n",
    "\n",
    "y = df['Transported']\n",
    "X = df.drop(features_eliminadas + ['Transported'], axis = 1).astype(int)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3, random_state= 42)\n",
    "\n",
    "eval_set = [(X_train, y_train), (X_test, y_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | learni... | max_depth | min_sa... | min_sa... | n_esti... | subsample |\n",
      "-------------------------------------------------------------------------------------------------\n",
      "| \u001b[39m1        \u001b[39m | \u001b[39m0.7905   \u001b[39m | \u001b[39m0.1186   \u001b[39m | \u001b[39m9.655    \u001b[39m | \u001b[39m14.91    \u001b[39m | \u001b[39m12.78    \u001b[39m | \u001b[39m89.0     \u001b[39m | \u001b[39m0.2404   \u001b[39m |\n",
      "| \u001b[35m2        \u001b[39m | \u001b[35m0.7909   \u001b[39m | \u001b[35m0.02684  \u001b[39m | \u001b[35m9.063    \u001b[39m | \u001b[35m12.42    \u001b[39m | \u001b[35m14.75    \u001b[39m | \u001b[35m55.15    \u001b[39m | \u001b[35m0.9729   \u001b[39m |\n",
      "| \u001b[39m3        \u001b[39m | \u001b[39m0.7905   \u001b[39m | \u001b[39m0.2514   \u001b[39m | \u001b[39m4.486    \u001b[39m | \u001b[39m4.455    \u001b[39m | \u001b[39m5.301    \u001b[39m | \u001b[39m126.1    \u001b[39m | \u001b[39m0.5723   \u001b[39m |\n",
      "| \u001b[35m4        \u001b[39m | \u001b[35m0.7963   \u001b[39m | \u001b[35m0.1353   \u001b[39m | \u001b[35m5.039    \u001b[39m | \u001b[35m12.63    \u001b[39m | \u001b[35m4.511    \u001b[39m | \u001b[35m123.0    \u001b[39m | \u001b[35m0.4297   \u001b[39m |\n",
      "| \u001b[39m5        \u001b[39m | \u001b[39m0.7697   \u001b[39m | \u001b[39m0.1423   \u001b[39m | \u001b[39m8.496    \u001b[39m | \u001b[39m4.794    \u001b[39m | \u001b[39m11.26    \u001b[39m | \u001b[39m198.1    \u001b[39m | \u001b[39m0.1418   \u001b[39m |\n",
      "| \u001b[39m6        \u001b[39m | \u001b[39m0.7955   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m10.0     \u001b[39m | \u001b[39m20.0     \u001b[39m | \u001b[39m20.0     \u001b[39m | \u001b[39m117.0    \u001b[39m | \u001b[39m0.1027   \u001b[39m |\n",
      "| \u001b[39m7        \u001b[39m | \u001b[39m0.7809   \u001b[39m | \u001b[39m0.1975   \u001b[39m | \u001b[39m6.702    \u001b[39m | \u001b[39m17.53    \u001b[39m | \u001b[39m19.4     \u001b[39m | \u001b[39m299.8    \u001b[39m | \u001b[39m0.7833   \u001b[39m |\n",
      "| \u001b[39m8        \u001b[39m | \u001b[39m0.7847   \u001b[39m | \u001b[39m0.14     \u001b[39m | \u001b[39m5.265    \u001b[39m | \u001b[39m11.44    \u001b[39m | \u001b[39m5.041    \u001b[39m | \u001b[39m123.1    \u001b[39m | \u001b[39m0.5494   \u001b[39m |\n",
      "| \u001b[39m9        \u001b[39m | \u001b[39m0.7834   \u001b[39m | \u001b[39m0.1849   \u001b[39m | \u001b[39m4.734    \u001b[39m | \u001b[39m8.066    \u001b[39m | \u001b[39m17.16    \u001b[39m | \u001b[39m200.5    \u001b[39m | \u001b[39m0.5885   \u001b[39m |\n",
      "| \u001b[39m10       \u001b[39m | \u001b[39m0.793    \u001b[39m | \u001b[39m0.1026   \u001b[39m | \u001b[39m4.909    \u001b[39m | \u001b[39m12.59    \u001b[39m | \u001b[39m15.74    \u001b[39m | \u001b[39m113.9    \u001b[39m | \u001b[39m0.7603   \u001b[39m |\n",
      "| \u001b[39m11       \u001b[39m | \u001b[39m0.7859   \u001b[39m | \u001b[39m0.1298   \u001b[39m | \u001b[39m7.584    \u001b[39m | \u001b[39m8.914    \u001b[39m | \u001b[39m13.02    \u001b[39m | \u001b[39m117.3    \u001b[39m | \u001b[39m0.5358   \u001b[39m |\n",
      "| \u001b[39m12       \u001b[39m | \u001b[39m0.788    \u001b[39m | \u001b[39m0.148    \u001b[39m | \u001b[39m5.051    \u001b[39m | \u001b[39m12.64    \u001b[39m | \u001b[39m4.524    \u001b[39m | \u001b[39m123.0    \u001b[39m | \u001b[39m0.4425   \u001b[39m |\n",
      "| \u001b[39m13       \u001b[39m | \u001b[39m0.7909   \u001b[39m | \u001b[39m0.1278   \u001b[39m | \u001b[39m5.031    \u001b[39m | \u001b[39m12.62    \u001b[39m | \u001b[39m4.503    \u001b[39m | \u001b[39m123.0    \u001b[39m | \u001b[39m0.422    \u001b[39m |\n",
      "| \u001b[39m14       \u001b[39m | \u001b[39m0.7888   \u001b[39m | \u001b[39m0.08749  \u001b[39m | \u001b[39m3.602    \u001b[39m | \u001b[39m19.97    \u001b[39m | \u001b[39m6.847    \u001b[39m | \u001b[39m264.6    \u001b[39m | \u001b[39m0.1357   \u001b[39m |\n",
      "| \u001b[39m15       \u001b[39m | \u001b[39m0.7847   \u001b[39m | \u001b[39m0.1392   \u001b[39m | \u001b[39m7.113    \u001b[39m | \u001b[39m16.52    \u001b[39m | \u001b[39m3.575    \u001b[39m | \u001b[39m295.4    \u001b[39m | \u001b[39m0.778    \u001b[39m |\n",
      "| \u001b[39m16       \u001b[39m | \u001b[39m0.7938   \u001b[39m | \u001b[39m0.06941  \u001b[39m | \u001b[39m5.772    \u001b[39m | \u001b[39m8.844    \u001b[39m | \u001b[39m8.608    \u001b[39m | \u001b[39m124.7    \u001b[39m | \u001b[39m0.9814   \u001b[39m |\n",
      "| \u001b[39m17       \u001b[39m | \u001b[39m0.7868   \u001b[39m | \u001b[39m0.1625   \u001b[39m | \u001b[39m7.08     \u001b[39m | \u001b[39m11.81    \u001b[39m | \u001b[39m15.95    \u001b[39m | \u001b[39m147.9    \u001b[39m | \u001b[39m0.5233   \u001b[39m |\n",
      "| \u001b[39m18       \u001b[39m | \u001b[39m0.7751   \u001b[39m | \u001b[39m0.1959   \u001b[39m | \u001b[39m5.481    \u001b[39m | \u001b[39m17.3     \u001b[39m | \u001b[39m14.87    \u001b[39m | \u001b[39m217.1    \u001b[39m | \u001b[39m0.6048   \u001b[39m |\n",
      "| \u001b[35m19       \u001b[39m | \u001b[35m0.798    \u001b[39m | \u001b[35m0.08138  \u001b[39m | \u001b[35m3.526    \u001b[39m | \u001b[35m4.179    \u001b[39m | \u001b[35m9.076    \u001b[39m | \u001b[35m98.4     \u001b[39m | \u001b[35m0.4379   \u001b[39m |\n",
      "| \u001b[39m20       \u001b[39m | \u001b[39m0.7888   \u001b[39m | \u001b[39m0.121    \u001b[39m | \u001b[39m3.193    \u001b[39m | \u001b[39m3.271    \u001b[39m | \u001b[39m19.73    \u001b[39m | \u001b[39m235.9    \u001b[39m | \u001b[39m0.1557   \u001b[39m |\n",
      "| \u001b[39m21       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.03686  \u001b[39m | \u001b[39m9.093    \u001b[39m | \u001b[39m11.72    \u001b[39m | \u001b[39m11.92    \u001b[39m | \u001b[39m172.1    \u001b[39m | \u001b[39m0.3335   \u001b[39m |\n",
      "| \u001b[39m22       \u001b[39m | \u001b[39m0.7901   \u001b[39m | \u001b[39m0.2554   \u001b[39m | \u001b[39m8.859    \u001b[39m | \u001b[39m19.12    \u001b[39m | \u001b[39m15.95    \u001b[39m | \u001b[39m181.3    \u001b[39m | \u001b[39m0.5802   \u001b[39m |\n",
      "| \u001b[39m23       \u001b[39m | \u001b[39m0.793    \u001b[39m | \u001b[39m0.2144   \u001b[39m | \u001b[39m8.504    \u001b[39m | \u001b[39m13.85    \u001b[39m | \u001b[39m2.969    \u001b[39m | \u001b[39m245.5    \u001b[39m | \u001b[39m0.7327   \u001b[39m |\n",
      "| \u001b[39m24       \u001b[39m | \u001b[39m0.7972   \u001b[39m | \u001b[39m0.01367  \u001b[39m | \u001b[39m7.403    \u001b[39m | \u001b[39m17.34    \u001b[39m | \u001b[39m4.219    \u001b[39m | \u001b[39m237.2    \u001b[39m | \u001b[39m0.2608   \u001b[39m |\n",
      "| \u001b[39m25       \u001b[39m | \u001b[39m0.7947   \u001b[39m | \u001b[39m0.1152   \u001b[39m | \u001b[39m9.632    \u001b[39m | \u001b[39m2.169    \u001b[39m | \u001b[39m7.79     \u001b[39m | \u001b[39m52.02    \u001b[39m | \u001b[39m0.9287   \u001b[39m |\n",
      "| \u001b[39m26       \u001b[39m | \u001b[39m0.7959   \u001b[39m | \u001b[39m0.0391   \u001b[39m | \u001b[39m6.96     \u001b[39m | \u001b[39m17.02    \u001b[39m | \u001b[39m19.4     \u001b[39m | \u001b[39m131.4    \u001b[39m | \u001b[39m0.5837   \u001b[39m |\n",
      "| \u001b[35m27       \u001b[39m | \u001b[35m0.7993   \u001b[39m | \u001b[35m0.01804  \u001b[39m | \u001b[35m5.203    \u001b[39m | \u001b[35m6.682    \u001b[39m | \u001b[35m10.85    \u001b[39m | \u001b[35m74.58    \u001b[39m | \u001b[35m0.2797   \u001b[39m |\n",
      "| \u001b[39m28       \u001b[39m | \u001b[39m0.7972   \u001b[39m | \u001b[39m0.02961  \u001b[39m | \u001b[39m9.954    \u001b[39m | \u001b[39m14.72    \u001b[39m | \u001b[39m7.826    \u001b[39m | \u001b[39m105.9    \u001b[39m | \u001b[39m0.2174   \u001b[39m |\n",
      "| \u001b[39m29       \u001b[39m | \u001b[39m0.7826   \u001b[39m | \u001b[39m0.1889   \u001b[39m | \u001b[39m6.503    \u001b[39m | \u001b[39m13.96    \u001b[39m | \u001b[39m10.55    \u001b[39m | \u001b[39m194.4    \u001b[39m | \u001b[39m0.8772   \u001b[39m |\n",
      "| \u001b[39m30       \u001b[39m | \u001b[39m0.7863   \u001b[39m | \u001b[39m0.103    \u001b[39m | \u001b[39m5.427    \u001b[39m | \u001b[39m16.61    \u001b[39m | \u001b[39m18.49    \u001b[39m | \u001b[39m271.2    \u001b[39m | \u001b[39m0.9212   \u001b[39m |\n",
      "| \u001b[39m31       \u001b[39m | \u001b[39m0.783    \u001b[39m | \u001b[39m0.09561  \u001b[39m | \u001b[39m7.15     \u001b[39m | \u001b[39m18.13    \u001b[39m | \u001b[39m3.16     \u001b[39m | \u001b[39m280.0    \u001b[39m | \u001b[39m0.1837   \u001b[39m |\n",
      "| \u001b[39m32       \u001b[39m | \u001b[39m0.7809   \u001b[39m | \u001b[39m0.09893  \u001b[39m | \u001b[39m8.901    \u001b[39m | \u001b[39m7.038    \u001b[39m | \u001b[39m4.872    \u001b[39m | \u001b[39m211.8    \u001b[39m | \u001b[39m0.1555   \u001b[39m |\n",
      "| \u001b[39m33       \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.2758   \u001b[39m | \u001b[39m7.168    \u001b[39m | \u001b[39m10.31    \u001b[39m | \u001b[39m17.38    \u001b[39m | \u001b[39m68.97    \u001b[39m | \u001b[39m0.9214   \u001b[39m |\n",
      "| \u001b[39m34       \u001b[39m | \u001b[39m0.7738   \u001b[39m | \u001b[39m0.2693   \u001b[39m | \u001b[39m8.748    \u001b[39m | \u001b[39m9.846    \u001b[39m | \u001b[39m10.44    \u001b[39m | \u001b[39m286.0    \u001b[39m | \u001b[39m0.4615   \u001b[39m |\n",
      "| \u001b[39m35       \u001b[39m | \u001b[39m0.7963   \u001b[39m | \u001b[39m0.03493  \u001b[39m | \u001b[39m5.132    \u001b[39m | \u001b[39m6.081    \u001b[39m | \u001b[39m6.467    \u001b[39m | \u001b[39m113.4    \u001b[39m | \u001b[39m0.7724   \u001b[39m |\n",
      "| \u001b[39m36       \u001b[39m | \u001b[39m0.7763   \u001b[39m | \u001b[39m0.2816   \u001b[39m | \u001b[39m5.665    \u001b[39m | \u001b[39m5.623    \u001b[39m | \u001b[39m5.274    \u001b[39m | \u001b[39m276.4    \u001b[39m | \u001b[39m0.386    \u001b[39m |\n",
      "| \u001b[39m37       \u001b[39m | \u001b[39m0.7847   \u001b[39m | \u001b[39m0.06491  \u001b[39m | \u001b[39m8.706    \u001b[39m | \u001b[39m3.084    \u001b[39m | \u001b[39m3.004    \u001b[39m | \u001b[39m100.2    \u001b[39m | \u001b[39m0.6912   \u001b[39m |\n",
      "| \u001b[39m38       \u001b[39m | \u001b[39m0.7905   \u001b[39m | \u001b[39m0.2901   \u001b[39m | \u001b[39m5.797    \u001b[39m | \u001b[39m15.18    \u001b[39m | \u001b[39m18.8     \u001b[39m | \u001b[39m271.6    \u001b[39m | \u001b[39m0.96     \u001b[39m |\n",
      "| \u001b[39m39       \u001b[39m | \u001b[39m0.7843   \u001b[39m | \u001b[39m0.252    \u001b[39m | \u001b[39m5.092    \u001b[39m | \u001b[39m10.05    \u001b[39m | \u001b[39m7.941    \u001b[39m | \u001b[39m244.3    \u001b[39m | \u001b[39m0.4839   \u001b[39m |\n",
      "| \u001b[39m40       \u001b[39m | \u001b[39m0.7918   \u001b[39m | \u001b[39m0.1221   \u001b[39m | \u001b[39m7.143    \u001b[39m | \u001b[39m14.78    \u001b[39m | \u001b[39m16.16    \u001b[39m | \u001b[39m70.87    \u001b[39m | \u001b[39m0.1164   \u001b[39m |\n",
      "| \u001b[39m41       \u001b[39m | \u001b[39m0.7901   \u001b[39m | \u001b[39m0.1551   \u001b[39m | \u001b[39m3.166    \u001b[39m | \u001b[39m1.108    \u001b[39m | \u001b[39m5.177    \u001b[39m | \u001b[39m161.2    \u001b[39m | \u001b[39m0.4369   \u001b[39m |\n",
      "| \u001b[39m42       \u001b[39m | \u001b[39m0.7868   \u001b[39m | \u001b[39m0.2434   \u001b[39m | \u001b[39m6.881    \u001b[39m | \u001b[39m16.91    \u001b[39m | \u001b[39m15.31    \u001b[39m | \u001b[39m277.0    \u001b[39m | \u001b[39m0.6033   \u001b[39m |\n",
      "| \u001b[39m43       \u001b[39m | \u001b[39m0.7976   \u001b[39m | \u001b[39m0.1913   \u001b[39m | \u001b[39m5.826    \u001b[39m | \u001b[39m12.05    \u001b[39m | \u001b[39m2.821    \u001b[39m | \u001b[39m67.04    \u001b[39m | \u001b[39m0.8072   \u001b[39m |\n",
      "| \u001b[39m44       \u001b[39m | \u001b[39m0.7859   \u001b[39m | \u001b[39m0.179    \u001b[39m | \u001b[39m6.392    \u001b[39m | \u001b[39m15.71    \u001b[39m | \u001b[39m3.282    \u001b[39m | \u001b[39m236.0    \u001b[39m | \u001b[39m0.6978   \u001b[39m |\n",
      "| \u001b[39m45       \u001b[39m | \u001b[39m0.7834   \u001b[39m | \u001b[39m0.233    \u001b[39m | \u001b[39m3.276    \u001b[39m | \u001b[39m7.442    \u001b[39m | \u001b[39m2.192    \u001b[39m | \u001b[39m240.8    \u001b[39m | \u001b[39m0.4356   \u001b[39m |\n",
      "| \u001b[39m46       \u001b[39m | \u001b[39m0.783    \u001b[39m | \u001b[39m0.2566   \u001b[39m | \u001b[39m9.659    \u001b[39m | \u001b[39m19.99    \u001b[39m | \u001b[39m17.1     \u001b[39m | \u001b[39m295.9    \u001b[39m | \u001b[39m0.7812   \u001b[39m |\n",
      "| \u001b[39m47       \u001b[39m | \u001b[39m0.7822   \u001b[39m | \u001b[39m0.2932   \u001b[39m | \u001b[39m6.741    \u001b[39m | \u001b[39m2.775    \u001b[39m | \u001b[39m18.91    \u001b[39m | \u001b[39m253.6    \u001b[39m | \u001b[39m0.8001   \u001b[39m |\n",
      "| \u001b[35m48       \u001b[39m | \u001b[35m0.8001   \u001b[39m | \u001b[35m0.03646  \u001b[39m | \u001b[35m5.221    \u001b[39m | \u001b[35m6.7      \u001b[39m | \u001b[35m10.87    \u001b[39m | \u001b[35m74.6     \u001b[39m | \u001b[35m0.2981   \u001b[39m |\n",
      "| \u001b[39m49       \u001b[39m | \u001b[39m0.7955   \u001b[39m | \u001b[39m0.0446   \u001b[39m | \u001b[39m5.209    \u001b[39m | \u001b[39m6.688    \u001b[39m | \u001b[39m10.85    \u001b[39m | \u001b[39m74.59    \u001b[39m | \u001b[39m0.286    \u001b[39m |\n",
      "| \u001b[39m50       \u001b[39m | \u001b[39m0.7876   \u001b[39m | \u001b[39m0.01     \u001b[39m | \u001b[39m5.216    \u001b[39m | \u001b[39m6.695    \u001b[39m | \u001b[39m10.86    \u001b[39m | \u001b[39m74.6     \u001b[39m | \u001b[39m0.293    \u001b[39m |\n",
      "| \u001b[39m51       \u001b[39m | \u001b[39m0.7943   \u001b[39m | \u001b[39m0.06696  \u001b[39m | \u001b[39m8.765    \u001b[39m | \u001b[39m1.404    \u001b[39m | \u001b[39m6.767    \u001b[39m | \u001b[39m214.3    \u001b[39m | \u001b[39m0.5638   \u001b[39m |\n",
      "| \u001b[39m52       \u001b[39m | \u001b[39m0.7988   \u001b[39m | \u001b[39m0.05359  \u001b[39m | \u001b[39m5.221    \u001b[39m | \u001b[39m6.7      \u001b[39m | \u001b[39m10.86    \u001b[39m | \u001b[39m74.6     \u001b[39m | \u001b[39m0.2979   \u001b[39m |\n",
      "| \u001b[39m53       \u001b[39m | \u001b[39m0.7776   \u001b[39m | \u001b[39m0.2652   \u001b[39m | \u001b[39m5.218    \u001b[39m | \u001b[39m6.696    \u001b[39m | \u001b[39m10.86    \u001b[39m | \u001b[39m74.6     \u001b[39m | \u001b[39m0.2951   \u001b[39m |\n",
      "| \u001b[39m54       \u001b[39m | \u001b[39m0.7713   \u001b[39m | \u001b[39m0.2894   \u001b[39m | \u001b[39m6.174    \u001b[39m | \u001b[39m19.7     \u001b[39m | \u001b[39m10.88    \u001b[39m | \u001b[39m184.7    \u001b[39m | \u001b[39m0.4605   \u001b[39m |\n",
      "| \u001b[39m55       \u001b[39m | \u001b[39m0.7963   \u001b[39m | \u001b[39m0.04357  \u001b[39m | \u001b[39m5.227    \u001b[39m | \u001b[39m6.705    \u001b[39m | \u001b[39m10.87    \u001b[39m | \u001b[39m74.61    \u001b[39m | \u001b[39m0.2794   \u001b[39m |\n",
      "| \u001b[39m56       \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.1906   \u001b[39m | \u001b[39m4.122    \u001b[39m | \u001b[39m5.978    \u001b[39m | \u001b[39m17.69    \u001b[39m | \u001b[39m76.5     \u001b[39m | \u001b[39m0.2913   \u001b[39m |\n",
      "| \u001b[39m57       \u001b[39m | \u001b[39m0.7951   \u001b[39m | \u001b[39m0.08342  \u001b[39m | \u001b[39m4.442    \u001b[39m | \u001b[39m17.67    \u001b[39m | \u001b[39m12.9     \u001b[39m | \u001b[39m289.1    \u001b[39m | \u001b[39m0.695    \u001b[39m |\n",
      "| \u001b[39m58       \u001b[39m | \u001b[39m0.7863   \u001b[39m | \u001b[39m0.2658   \u001b[39m | \u001b[39m5.889    \u001b[39m | \u001b[39m10.67    \u001b[39m | \u001b[39m19.4     \u001b[39m | \u001b[39m172.4    \u001b[39m | \u001b[39m0.9507   \u001b[39m |\n",
      "| \u001b[39m59       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.02723  \u001b[39m | \u001b[39m3.83     \u001b[39m | \u001b[39m7.896    \u001b[39m | \u001b[39m10.4     \u001b[39m | \u001b[39m117.3    \u001b[39m | \u001b[39m0.7721   \u001b[39m |\n",
      "| \u001b[39m60       \u001b[39m | \u001b[39m0.7722   \u001b[39m | \u001b[39m0.2239   \u001b[39m | \u001b[39m7.173    \u001b[39m | \u001b[39m19.47    \u001b[39m | \u001b[39m5.034    \u001b[39m | \u001b[39m247.0    \u001b[39m | \u001b[39m0.189    \u001b[39m |\n",
      "| \u001b[39m61       \u001b[39m | \u001b[39m0.7901   \u001b[39m | \u001b[39m0.01933  \u001b[39m | \u001b[39m6.628    \u001b[39m | \u001b[39m3.167    \u001b[39m | \u001b[39m4.572    \u001b[39m | \u001b[39m104.5    \u001b[39m | \u001b[39m0.9881   \u001b[39m |\n",
      "| \u001b[39m62       \u001b[39m | \u001b[39m0.7909   \u001b[39m | \u001b[39m0.2493   \u001b[39m | \u001b[39m3.516    \u001b[39m | \u001b[39m11.55    \u001b[39m | \u001b[39m9.465    \u001b[39m | \u001b[39m125.5    \u001b[39m | \u001b[39m0.9287   \u001b[39m |\n",
      "| \u001b[39m63       \u001b[39m | \u001b[39m0.783    \u001b[39m | \u001b[39m0.1271   \u001b[39m | \u001b[39m9.042    \u001b[39m | \u001b[39m11.11    \u001b[39m | \u001b[39m18.98    \u001b[39m | \u001b[39m88.2     \u001b[39m | \u001b[39m0.2648   \u001b[39m |\n",
      "| \u001b[39m64       \u001b[39m | \u001b[39m0.7576   \u001b[39m | \u001b[39m0.2995   \u001b[39m | \u001b[39m7.808    \u001b[39m | \u001b[39m14.79    \u001b[39m | \u001b[39m2.723    \u001b[39m | \u001b[39m164.3    \u001b[39m | \u001b[39m0.1993   \u001b[39m |\n",
      "| \u001b[39m65       \u001b[39m | \u001b[39m0.7888   \u001b[39m | \u001b[39m0.02021  \u001b[39m | \u001b[39m4.206    \u001b[39m | \u001b[39m14.32    \u001b[39m | \u001b[39m10.21    \u001b[39m | \u001b[39m65.11    \u001b[39m | \u001b[39m0.5801   \u001b[39m |\n",
      "| \u001b[39m66       \u001b[39m | \u001b[39m0.7943   \u001b[39m | \u001b[39m0.1552   \u001b[39m | \u001b[39m3.001    \u001b[39m | \u001b[39m15.4     \u001b[39m | \u001b[39m15.42    \u001b[39m | \u001b[39m57.02    \u001b[39m | \u001b[39m0.9529   \u001b[39m |\n",
      "| \u001b[39m67       \u001b[39m | \u001b[39m0.7963   \u001b[39m | \u001b[39m0.1571   \u001b[39m | \u001b[39m8.604    \u001b[39m | \u001b[39m2.137    \u001b[39m | \u001b[39m15.68    \u001b[39m | \u001b[39m53.71    \u001b[39m | \u001b[39m0.4402   \u001b[39m |\n",
      "| \u001b[39m68       \u001b[39m | \u001b[39m0.7651   \u001b[39m | \u001b[39m0.2935   \u001b[39m | \u001b[39m7.41     \u001b[39m | \u001b[39m12.87    \u001b[39m | \u001b[39m16.88    \u001b[39m | \u001b[39m199.7    \u001b[39m | \u001b[39m0.2931   \u001b[39m |\n",
      "| \u001b[39m69       \u001b[39m | \u001b[39m0.7968   \u001b[39m | \u001b[39m0.05558  \u001b[39m | \u001b[39m3.14     \u001b[39m | \u001b[39m17.85    \u001b[39m | \u001b[39m13.06    \u001b[39m | \u001b[39m232.2    \u001b[39m | \u001b[39m0.8768   \u001b[39m |\n",
      "| \u001b[39m70       \u001b[39m | \u001b[39m0.7963   \u001b[39m | \u001b[39m0.0403   \u001b[39m | \u001b[39m6.675    \u001b[39m | \u001b[39m5.482    \u001b[39m | \u001b[39m4.48     \u001b[39m | \u001b[39m108.5    \u001b[39m | \u001b[39m0.811    \u001b[39m |\n",
      "| \u001b[39m71       \u001b[39m | \u001b[39m0.7872   \u001b[39m | \u001b[39m0.1617   \u001b[39m | \u001b[39m7.225    \u001b[39m | \u001b[39m9.514    \u001b[39m | \u001b[39m9.509    \u001b[39m | \u001b[39m109.1    \u001b[39m | \u001b[39m0.5118   \u001b[39m |\n",
      "| \u001b[39m72       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.08604  \u001b[39m | \u001b[39m4.58     \u001b[39m | \u001b[39m15.63    \u001b[39m | \u001b[39m15.44    \u001b[39m | \u001b[39m238.4    \u001b[39m | \u001b[39m0.727    \u001b[39m |\n",
      "| \u001b[39m73       \u001b[39m | \u001b[39m0.7793   \u001b[39m | \u001b[39m0.2467   \u001b[39m | \u001b[39m5.492    \u001b[39m | \u001b[39m6.74     \u001b[39m | \u001b[39m16.37    \u001b[39m | \u001b[39m163.8    \u001b[39m | \u001b[39m0.5913   \u001b[39m |\n",
      "| \u001b[39m74       \u001b[39m | \u001b[39m0.7868   \u001b[39m | \u001b[39m0.1688   \u001b[39m | \u001b[39m6.759    \u001b[39m | \u001b[39m19.29    \u001b[39m | \u001b[39m5.617    \u001b[39m | \u001b[39m280.5    \u001b[39m | \u001b[39m0.9835   \u001b[39m |\n",
      "| \u001b[39m75       \u001b[39m | \u001b[39m0.793    \u001b[39m | \u001b[39m0.05987  \u001b[39m | \u001b[39m5.829    \u001b[39m | \u001b[39m14.33    \u001b[39m | \u001b[39m6.863    \u001b[39m | \u001b[39m271.5    \u001b[39m | \u001b[39m0.4641   \u001b[39m |\n",
      "| \u001b[39m76       \u001b[39m | \u001b[39m0.7955   \u001b[39m | \u001b[39m0.2582   \u001b[39m | \u001b[39m5.251    \u001b[39m | \u001b[39m1.567    \u001b[39m | \u001b[39m14.02    \u001b[39m | \u001b[39m52.52    \u001b[39m | \u001b[39m0.7207   \u001b[39m |\n",
      "| \u001b[39m77       \u001b[39m | \u001b[39m0.7926   \u001b[39m | \u001b[39m0.01699  \u001b[39m | \u001b[39m4.929    \u001b[39m | \u001b[39m11.76    \u001b[39m | \u001b[39m6.582    \u001b[39m | \u001b[39m149.4    \u001b[39m | \u001b[39m0.1171   \u001b[39m |\n",
      "| \u001b[39m78       \u001b[39m | \u001b[39m0.7901   \u001b[39m | \u001b[39m0.2908   \u001b[39m | \u001b[39m8.786    \u001b[39m | \u001b[39m17.66    \u001b[39m | \u001b[39m11.21    \u001b[39m | \u001b[39m147.4    \u001b[39m | \u001b[39m0.9444   \u001b[39m |\n",
      "| \u001b[39m79       \u001b[39m | \u001b[39m0.7893   \u001b[39m | \u001b[39m0.2109   \u001b[39m | \u001b[39m7.528    \u001b[39m | \u001b[39m18.96    \u001b[39m | \u001b[39m14.6     \u001b[39m | \u001b[39m294.8    \u001b[39m | \u001b[39m0.9832   \u001b[39m |\n",
      "| \u001b[39m80       \u001b[39m | \u001b[39m0.7893   \u001b[39m | \u001b[39m0.1055   \u001b[39m | \u001b[39m9.098    \u001b[39m | \u001b[39m8.9      \u001b[39m | \u001b[39m4.022    \u001b[39m | \u001b[39m106.0    \u001b[39m | \u001b[39m0.8366   \u001b[39m |\n",
      "| \u001b[39m81       \u001b[39m | \u001b[39m0.7834   \u001b[39m | \u001b[39m0.2206   \u001b[39m | \u001b[39m8.931    \u001b[39m | \u001b[39m18.59    \u001b[39m | \u001b[39m7.603    \u001b[39m | \u001b[39m258.5    \u001b[39m | \u001b[39m0.7727   \u001b[39m |\n",
      "| \u001b[39m82       \u001b[39m | \u001b[39m0.7918   \u001b[39m | \u001b[39m0.169    \u001b[39m | \u001b[39m5.55     \u001b[39m | \u001b[39m4.803    \u001b[39m | \u001b[39m3.257    \u001b[39m | \u001b[39m74.63    \u001b[39m | \u001b[39m0.9816   \u001b[39m |\n",
      "| \u001b[39m83       \u001b[39m | \u001b[39m0.7872   \u001b[39m | \u001b[39m0.1672   \u001b[39m | \u001b[39m8.259    \u001b[39m | \u001b[39m15.46    \u001b[39m | \u001b[39m18.49    \u001b[39m | \u001b[39m102.9    \u001b[39m | \u001b[39m0.6131   \u001b[39m |\n",
      "| \u001b[39m84       \u001b[39m | \u001b[39m0.7901   \u001b[39m | \u001b[39m0.2144   \u001b[39m | \u001b[39m5.808    \u001b[39m | \u001b[39m13.26    \u001b[39m | \u001b[39m13.79    \u001b[39m | \u001b[39m76.04    \u001b[39m | \u001b[39m0.2724   \u001b[39m |\n",
      "| \u001b[39m85       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.1259   \u001b[39m | \u001b[39m9.86     \u001b[39m | \u001b[39m8.718    \u001b[39m | \u001b[39m7.421    \u001b[39m | \u001b[39m274.1    \u001b[39m | \u001b[39m0.6665   \u001b[39m |\n",
      "| \u001b[39m86       \u001b[39m | \u001b[39m0.7905   \u001b[39m | \u001b[39m0.0424   \u001b[39m | \u001b[39m8.0      \u001b[39m | \u001b[39m11.12    \u001b[39m | \u001b[39m11.4     \u001b[39m | \u001b[39m229.3    \u001b[39m | \u001b[39m0.207    \u001b[39m |\n",
      "| \u001b[39m87       \u001b[39m | \u001b[39m0.7913   \u001b[39m | \u001b[39m0.01175  \u001b[39m | \u001b[39m6.324    \u001b[39m | \u001b[39m5.093    \u001b[39m | \u001b[39m18.66    \u001b[39m | \u001b[39m57.95    \u001b[39m | \u001b[39m0.1732   \u001b[39m |\n",
      "| \u001b[39m88       \u001b[39m | \u001b[39m0.7951   \u001b[39m | \u001b[39m0.1874   \u001b[39m | \u001b[39m9.191    \u001b[39m | \u001b[39m10.61    \u001b[39m | \u001b[39m7.451    \u001b[39m | \u001b[39m115.6    \u001b[39m | \u001b[39m0.659    \u001b[39m |\n",
      "| \u001b[39m89       \u001b[39m | \u001b[39m0.788    \u001b[39m | \u001b[39m0.1652   \u001b[39m | \u001b[39m4.002    \u001b[39m | \u001b[39m13.86    \u001b[39m | \u001b[39m9.787    \u001b[39m | \u001b[39m127.2    \u001b[39m | \u001b[39m0.9493   \u001b[39m |\n",
      "| \u001b[39m90       \u001b[39m | \u001b[39m0.7905   \u001b[39m | \u001b[39m0.1966   \u001b[39m | \u001b[39m3.31     \u001b[39m | \u001b[39m7.153    \u001b[39m | \u001b[39m10.12    \u001b[39m | \u001b[39m75.31    \u001b[39m | \u001b[39m0.4854   \u001b[39m |\n",
      "| \u001b[39m91       \u001b[39m | \u001b[39m0.7747   \u001b[39m | \u001b[39m0.2219   \u001b[39m | \u001b[39m5.487    \u001b[39m | \u001b[39m17.9     \u001b[39m | \u001b[39m17.61    \u001b[39m | \u001b[39m268.8    \u001b[39m | \u001b[39m0.1911   \u001b[39m |\n",
      "| \u001b[39m92       \u001b[39m | \u001b[39m0.6847   \u001b[39m | \u001b[39m0.1784   \u001b[39m | \u001b[39m9.363    \u001b[39m | \u001b[39m2.188    \u001b[39m | \u001b[39m7.085    \u001b[39m | \u001b[39m93.88    \u001b[39m | \u001b[39m0.1016   \u001b[39m |\n",
      "| \u001b[39m93       \u001b[39m | \u001b[39m0.7539   \u001b[39m | \u001b[39m0.1752   \u001b[39m | \u001b[39m9.345    \u001b[39m | \u001b[39m14.76    \u001b[39m | \u001b[39m4.551    \u001b[39m | \u001b[39m274.3    \u001b[39m | \u001b[39m0.1263   \u001b[39m |\n",
      "| \u001b[39m94       \u001b[39m | \u001b[39m0.7893   \u001b[39m | \u001b[39m0.1159   \u001b[39m | \u001b[39m8.604    \u001b[39m | \u001b[39m15.41    \u001b[39m | \u001b[39m7.143    \u001b[39m | \u001b[39m67.76    \u001b[39m | \u001b[39m0.7699   \u001b[39m |\n",
      "| \u001b[39m95       \u001b[39m | \u001b[39m0.7984   \u001b[39m | \u001b[39m0.1161   \u001b[39m | \u001b[39m3.709    \u001b[39m | \u001b[39m8.714    \u001b[39m | \u001b[39m6.398    \u001b[39m | \u001b[39m254.6    \u001b[39m | \u001b[39m0.9626   \u001b[39m |\n",
      "| \u001b[39m96       \u001b[39m | \u001b[39m0.7897   \u001b[39m | \u001b[39m0.268    \u001b[39m | \u001b[39m7.437    \u001b[39m | \u001b[39m14.6     \u001b[39m | \u001b[39m10.29    \u001b[39m | \u001b[39m71.21    \u001b[39m | \u001b[39m0.9437   \u001b[39m |\n",
      "| \u001b[39m97       \u001b[39m | \u001b[39m0.788    \u001b[39m | \u001b[39m0.1263   \u001b[39m | \u001b[39m8.872    \u001b[39m | \u001b[39m13.16    \u001b[39m | \u001b[39m16.93    \u001b[39m | \u001b[39m193.9    \u001b[39m | \u001b[39m0.703    \u001b[39m |\n",
      "| \u001b[39m98       \u001b[39m | \u001b[39m0.7934   \u001b[39m | \u001b[39m0.1878   \u001b[39m | \u001b[39m7.684    \u001b[39m | \u001b[39m7.35     \u001b[39m | \u001b[39m10.52    \u001b[39m | \u001b[39m68.88    \u001b[39m | \u001b[39m0.5667   \u001b[39m |\n",
      "| \u001b[39m99       \u001b[39m | \u001b[39m0.7909   \u001b[39m | \u001b[39m0.02432  \u001b[39m | \u001b[39m6.119    \u001b[39m | \u001b[39m14.39    \u001b[39m | \u001b[39m2.063    \u001b[39m | \u001b[39m283.7    \u001b[39m | \u001b[39m0.6424   \u001b[39m |\n",
      "| \u001b[39m100      \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.06263  \u001b[39m | \u001b[39m7.621    \u001b[39m | \u001b[39m7.582    \u001b[39m | \u001b[39m10.68    \u001b[39m | \u001b[39m69.33    \u001b[39m | \u001b[39m0.8927   \u001b[39m |\n",
      "| \u001b[39m101      \u001b[39m | \u001b[39m0.7922   \u001b[39m | \u001b[39m0.08818  \u001b[39m | \u001b[39m5.997    \u001b[39m | \u001b[39m2.378    \u001b[39m | \u001b[39m4.67     \u001b[39m | \u001b[39m288.0    \u001b[39m | \u001b[39m0.1488   \u001b[39m |\n",
      "| \u001b[39m102      \u001b[39m | \u001b[39m0.7834   \u001b[39m | \u001b[39m0.195    \u001b[39m | \u001b[39m8.451    \u001b[39m | \u001b[39m6.629    \u001b[39m | \u001b[39m2.75     \u001b[39m | \u001b[39m255.5    \u001b[39m | \u001b[39m0.5571   \u001b[39m |\n",
      "| \u001b[39m103      \u001b[39m | \u001b[39m0.7855   \u001b[39m | \u001b[39m0.2162   \u001b[39m | \u001b[39m3.507    \u001b[39m | \u001b[39m19.11    \u001b[39m | \u001b[39m13.73    \u001b[39m | \u001b[39m260.6    \u001b[39m | \u001b[39m0.4718   \u001b[39m |\n",
      "| \u001b[39m104      \u001b[39m | \u001b[39m0.7709   \u001b[39m | \u001b[39m0.2456   \u001b[39m | \u001b[39m5.733    \u001b[39m | \u001b[39m7.973    \u001b[39m | \u001b[39m11.6     \u001b[39m | \u001b[39m280.2    \u001b[39m | \u001b[39m0.2668   \u001b[39m |\n",
      "| \u001b[39m105      \u001b[39m | \u001b[39m0.7634   \u001b[39m | \u001b[39m0.2383   \u001b[39m | \u001b[39m6.161    \u001b[39m | \u001b[39m6.112    \u001b[39m | \u001b[39m15.15    \u001b[39m | \u001b[39m151.3    \u001b[39m | \u001b[39m0.2974   \u001b[39m |\n",
      "=================================================================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.03646231217859132, max_depth=5,\n",
       "                           min_samples_leaf=6, min_samples_split=10,\n",
       "                           n_estimators=74, random_state=42,\n",
       "                           subsample=0.29808466023756697)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(learning_rate=0.03646231217859132, max_depth=5,\n",
       "                           min_samples_leaf=6, min_samples_split=10,\n",
       "                           n_estimators=74, random_state=42,\n",
       "                           subsample=0.29808466023756697)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.03646231217859132, max_depth=5,\n",
       "                           min_samples_leaf=6, min_samples_split=10,\n",
       "                           n_estimators=74, random_state=42,\n",
       "                           subsample=0.29808466023756697)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gbc_evaluate(n_estimators, max_depth, min_samples_split, min_samples_leaf, subsample, learning_rate):\n",
    "    model = GradientBoostingClassifier(\n",
    "        n_estimators=int(n_estimators),\n",
    "        max_depth=int(max_depth),\n",
    "        min_samples_split=int(min_samples_split),\n",
    "        min_samples_leaf=int(min_samples_leaf),\n",
    "        subsample=subsample,\n",
    "        learning_rate=learning_rate,\n",
    "        random_state=42\n",
    "    )\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    return accuracy  # Queremos minimizar el MSE\n",
    "\n",
    "# Definir los límites de los hiperparámetros a optimizar\n",
    "pbounds = {\n",
    "    'n_estimators': (50, 300),\n",
    "    'max_depth': (3, 10),\n",
    "    'min_samples_split': (2, 20),\n",
    "    'min_samples_leaf': (1, 20),\n",
    "    'subsample': (0.1, 1),\n",
    "    'learning_rate': (0.01, 0.3),\n",
    "}\n",
    "\n",
    "# Optimización Bayesiana\n",
    "optimizer = BayesianOptimization(\n",
    "    f=gbc_evaluate,\n",
    "    pbounds=pbounds,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "# Aumentar el número de iteraciones para maximizar la búsqueda\n",
    "optimizer.maximize(init_points=5, n_iter=100)\n",
    "\n",
    "# Una vez que la búsqueda ha terminado, obtenemos los mejores parámetros\n",
    "best_params = optimizer.max['params']\n",
    "best_params['n_estimators'] = int(best_params['n_estimators'])\n",
    "best_params['max_depth'] = int(best_params['max_depth'])\n",
    "best_params['min_samples_split'] = int(best_params['min_samples_split'])\n",
    "best_params['min_samples_leaf'] = int(best_params['min_samples_leaf'])\n",
    "\n",
    "# Entrenar el modelo final con los mejores parámetros\n",
    "final_model_2 = GradientBoostingClassifier(\n",
    "    **best_params,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "final_model_2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Puntajes de validación cruzada: [0.68269831 0.73125    0.79       0.814375   0.785     ]\n",
      "Media de los puntajes: 0.7606646627108058\n"
     ]
    }
   ],
   "source": [
    "y_pred = final_model_2.predict(X_test)\n",
    "\n",
    "# Validación cruzada\n",
    "cv_scores = cross_val_score(final_model, X, y, cv=5)\n",
    "\n",
    "print(f\"Puntajes de validación cruzada: {cv_scores}\")\n",
    "print(f\"Media de los puntajes: {cv_scores.mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del modelo: 0.8000832986255727\n",
      "Reporte de clasificación:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.82      0.77      0.79      1183\n",
      "        True       0.79      0.83      0.81      1218\n",
      "\n",
      "    accuracy                           0.80      2401\n",
      "   macro avg       0.80      0.80      0.80      2401\n",
      "weighted avg       0.80      0.80      0.80      2401\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "\n",
    "# Visualizar la matriz de confusión\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Clase 0\", \"Clase 1\"], yticklabels=[\"Clase 0\", \"Clase 1\"])\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "print(f\"Precisión del modelo: {accuracy}\")\n",
    "print(\"Reporte de clasificación:\\n\", class_report)\n",
    "\n",
    "# Calcular y graficar la curva ROC\n",
    "y_prob = final_model_2.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El ajuste parece mejor pero no mejora mucho la precisión del modelo. Podemos seguir valorando la exclusión de alguna variable pero probablemente solo consigamos bajar la precisión.\n",
    "\n",
    "Una vez entrenado podemos aplicar el modelo a los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alberto\\AppData\\Local\\Temp\\ipykernel_19144\\2936907257.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[1 0 1 ... 1 0 1]' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n",
      "  df_test.loc[:, 'CryoSleep'] = df_test['CryoSleep'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "df_t = pd.read_csv('Test_recoded.csv')\n",
    "\n",
    "df_test = df_t.drop(columns = ['Transported'] + columnas_no_usadas + features_eliminadas)\n",
    "\n",
    "df_test.loc[:, 'HomePlanet'] = df_test['HomePlanet'].map({'Europa': 0, 'Earth': 1, 'Mars': 2}).astype(int)\n",
    "df_test.loc[:, 'CabinDeck'] = df_test['CabinDeck'].map({'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'T': 7}).astype(int)\n",
    "df_test.loc[:, 'CabinSide'] = df_test['CabinSide'].map({'P': 0, 'S': 1}).astype(int)\n",
    "df_test.loc[:, 'CryoSleep'] = df_test['CryoSleep'].astype(int)\n",
    "df_test.loc[:, 'Destination'] = df_test['Destination'].map({'TRAPPIST-1e': 0, 'PSO J318.5-22': 1, '55 Cancri e': 2}).astype(int)\n",
    "\n",
    "df_test = df_test.astype(int)\n",
    "\n",
    "prediccion = final_model_2.predict(df_test)\n",
    "\n",
    "df_predict = pd.DataFrame({\n",
    "    'PassengerId': df_t['PassengerId'],\n",
    "    'Transported': prediccion.astype(bool)\n",
    "})\n",
    "\n",
    "df_predict.to_csv('prediccion_gb_nosolo.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usando optuna para buscar hiperparametros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Transported']\n",
    "X = df.drop(['Transported'], axis = 1).astype(int)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3, random_state= 42)\n",
    "\n",
    "eval_set = [(X_train, y_train), (X_test, y_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-09-25 05:16:28,621] A new study created in memory with name: no-name-cdf7fd24-f64f-4927-be80-999b54892e44\n",
      "[I 2024-09-25 05:16:32,254] Trial 0 finished with value: 0.7996425573216235 and parameters: {'n_estimators': 164, 'max_depth': 7, 'min_samples_split': 20, 'min_samples_leaf': 12, 'subsample': 0.2695144581497312, 'learning_rate': 0.11412632041066549}. Best is trial 0 with value: 0.7996425573216235.\n",
      "[I 2024-09-25 05:16:33,437] Trial 1 finished with value: 0.7505398764154233 and parameters: {'n_estimators': 185, 'max_depth': 3, 'min_samples_split': 17, 'min_samples_leaf': 2, 'subsample': 0.10090411852206589, 'learning_rate': 0.2369486207260915}. Best is trial 0 with value: 0.7996425573216235.\n",
      "[I 2024-09-25 05:16:42,558] Trial 2 finished with value: 0.8005343939309567 and parameters: {'n_estimators': 133, 'max_depth': 8, 'min_samples_split': 7, 'min_samples_leaf': 7, 'subsample': 0.9566952550703668, 'learning_rate': 0.23852133035430526}. Best is trial 2 with value: 0.8005343939309567.\n",
      "[I 2024-09-25 05:16:43,869] Trial 3 finished with value: 0.8005364988988148 and parameters: {'n_estimators': 101, 'max_depth': 3, 'min_samples_split': 5, 'min_samples_leaf': 20, 'subsample': 0.38043337918720443, 'learning_rate': 0.16842106298139028}. Best is trial 3 with value: 0.8005364988988148.\n",
      "[I 2024-09-25 05:16:45,370] Trial 4 finished with value: 0.7617854758365955 and parameters: {'n_estimators': 109, 'max_depth': 10, 'min_samples_split': 18, 'min_samples_leaf': 12, 'subsample': 0.12014671603940516, 'learning_rate': 0.2524671238617846}. Best is trial 3 with value: 0.8005364988988148.\n",
      "[I 2024-09-25 05:16:49,152] Trial 5 finished with value: 0.7985696934764941 and parameters: {'n_estimators': 220, 'max_depth': 3, 'min_samples_split': 15, 'min_samples_leaf': 6, 'subsample': 0.5497239684508507, 'learning_rate': 0.21225656651594305}. Best is trial 3 with value: 0.8005364988988148.\n",
      "[I 2024-09-25 05:16:52,834] Trial 6 finished with value: 0.8008925255079048 and parameters: {'n_estimators': 235, 'max_depth': 3, 'min_samples_split': 3, 'min_samples_leaf': 12, 'subsample': 0.49784930412447026, 'learning_rate': 0.14862683684817915}. Best is trial 6 with value: 0.8008925255079048.\n",
      "[I 2024-09-25 05:16:59,132] Trial 7 finished with value: 0.7996426530019808 and parameters: {'n_estimators': 107, 'max_depth': 10, 'min_samples_split': 13, 'min_samples_leaf': 5, 'subsample': 0.5450016244705179, 'learning_rate': 0.08499653046382918}. Best is trial 6 with value: 0.8008925255079048.\n",
      "[I 2024-09-25 05:17:12,599] Trial 8 finished with value: 0.8060713205209681 and parameters: {'n_estimators': 241, 'max_depth': 7, 'min_samples_split': 2, 'min_samples_leaf': 15, 'subsample': 0.982378160954553, 'learning_rate': 0.07910642198214694}. Best is trial 8 with value: 0.8060713205209681.\n",
      "[I 2024-09-25 05:17:18,277] Trial 9 finished with value: 0.8016054398492997 and parameters: {'n_estimators': 181, 'max_depth': 5, 'min_samples_split': 13, 'min_samples_leaf': 12, 'subsample': 0.6942583961408013, 'learning_rate': 0.16301814440856166}. Best is trial 8 with value: 0.8060713205209681.\n",
      "[I 2024-09-25 05:17:32,249] Trial 10 finished with value: 0.8044635077988103 and parameters: {'n_estimators': 281, 'max_depth': 6, 'min_samples_split': 10, 'min_samples_leaf': 18, 'subsample': 0.9664181659413371, 'learning_rate': 0.01170102014865812}. Best is trial 8 with value: 0.8060713205209681.\n",
      "[I 2024-09-25 05:17:46,915] Trial 11 finished with value: 0.8062493816656917 and parameters: {'n_estimators': 291, 'max_depth': 6, 'min_samples_split': 9, 'min_samples_leaf': 18, 'subsample': 0.9956568406327461, 'learning_rate': 0.012399660762452441}. Best is trial 11 with value: 0.8062493816656917.\n",
      "[I 2024-09-25 05:18:04,255] Trial 12 finished with value: 0.806606556439068 and parameters: {'n_estimators': 295, 'max_depth': 8, 'min_samples_split': 2, 'min_samples_leaf': 16, 'subsample': 0.7510590277145441, 'learning_rate': 0.01428612200391068}. Best is trial 12 with value: 0.806606556439068.\n",
      "[I 2024-09-25 05:18:21,507] Trial 13 finished with value: 0.8092855107599259 and parameters: {'n_estimators': 297, 'max_depth': 8, 'min_samples_split': 7, 'min_samples_leaf': 16, 'subsample': 0.7596052309728822, 'learning_rate': 0.011056400935966059}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:18:37,509] Trial 14 finished with value: 0.8060714162013253 and parameters: {'n_estimators': 268, 'max_depth': 9, 'min_samples_split': 5, 'min_samples_leaf': 16, 'subsample': 0.7575927886532862, 'learning_rate': 0.05314994433236915}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:18:53,671] Trial 15 finished with value: 0.7919627734520689 and parameters: {'n_estimators': 297, 'max_depth': 8, 'min_samples_split': 7, 'min_samples_leaf': 16, 'subsample': 0.7739329656822662, 'learning_rate': 0.2935469576506593}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:18:56,396] Trial 16 finished with value: 0.8046427171078201 and parameters: {'n_estimators': 53, 'max_depth': 8, 'min_samples_split': 4, 'min_samples_leaf': 20, 'subsample': 0.6922295607111238, 'learning_rate': 0.043625592154799095}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:19:12,933] Trial 17 finished with value: 0.8053567796135012 and parameters: {'n_estimators': 257, 'max_depth': 9, 'min_samples_split': 7, 'min_samples_leaf': 15, 'subsample': 0.8245381167283912, 'learning_rate': 0.05274998586674206}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:19:24,815] Trial 18 finished with value: 0.799106460280309 and parameters: {'n_estimators': 206, 'max_depth': 9, 'min_samples_split': 2, 'min_samples_leaf': 9, 'subsample': 0.6447610355750892, 'learning_rate': 0.11471985718841037}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:19:35,013] Trial 19 finished with value: 0.8073204275840346 and parameters: {'n_estimators': 262, 'max_depth': 5, 'min_samples_split': 9, 'min_samples_leaf': 14, 'subsample': 0.8607300068746163, 'learning_rate': 0.03495077759252839}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:19:44,981] Trial 20 finished with value: 0.8033923662001102 and parameters: {'n_estimators': 257, 'max_depth': 5, 'min_samples_split': 12, 'min_samples_leaf': 10, 'subsample': 0.8636066880647257, 'learning_rate': 0.09102255807624507}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:19:56,202] Trial 21 finished with value: 0.8060709377995394 and parameters: {'n_estimators': 297, 'max_depth': 5, 'min_samples_split': 9, 'min_samples_leaf': 14, 'subsample': 0.8408241724617366, 'learning_rate': 0.032315891047899906}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:20:04,685] Trial 22 finished with value: 0.8026779209730003 and parameters: {'n_estimators': 271, 'max_depth': 4, 'min_samples_split': 6, 'min_samples_leaf': 18, 'subsample': 0.8659696349705702, 'learning_rate': 0.0265944742161274}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:20:14,665] Trial 23 finished with value: 0.8060714162013253 and parameters: {'n_estimators': 234, 'max_depth': 7, 'min_samples_split': 9, 'min_samples_leaf': 14, 'subsample': 0.6439673333565284, 'learning_rate': 0.06534575086919366}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:20:27,084] Trial 24 finished with value: 0.803571097107334 and parameters: {'n_estimators': 300, 'max_depth': 6, 'min_samples_split': 11, 'min_samples_leaf': 17, 'subsample': 0.7367748565632307, 'learning_rate': 0.011750359430990794}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:20:43,275] Trial 25 finished with value: 0.7989274423320135 and parameters: {'n_estimators': 262, 'max_depth': 8, 'min_samples_split': 4, 'min_samples_leaf': 14, 'subsample': 0.9015139991214542, 'learning_rate': 0.11165232868481281}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:20:48,321] Trial 26 finished with value: 0.8053560141706436 and parameters: {'n_estimators': 205, 'max_depth': 4, 'min_samples_split': 8, 'min_samples_leaf': 19, 'subsample': 0.6296037147363707, 'learning_rate': 0.06477848375726039}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:21:02,416] Trial 27 finished with value: 0.8057142414279489 and parameters: {'n_estimators': 278, 'max_depth': 7, 'min_samples_split': 11, 'min_samples_leaf': 13, 'subsample': 0.8017511018028903, 'learning_rate': 0.033692877483012196}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:21:20,422] Trial 28 finished with value: 0.8039276977985672 and parameters: {'n_estimators': 242, 'max_depth': 9, 'min_samples_split': 5, 'min_samples_leaf': 9, 'subsample': 0.9170547683503327, 'learning_rate': 0.13759817915511016}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:21:29,193] Trial 29 finished with value: 0.7978572575368852 and parameters: {'n_estimators': 282, 'max_depth': 7, 'min_samples_split': 20, 'min_samples_leaf': 16, 'subsample': 0.44683283582653804, 'learning_rate': 0.11643818006616631}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:21:31,459] Trial 30 finished with value: 0.7955355736697608 and parameters: {'n_estimators': 165, 'max_depth': 4, 'min_samples_split': 14, 'min_samples_leaf': 17, 'subsample': 0.2565386272968836, 'learning_rate': 0.031084495489331586}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:21:45,260] Trial 31 finished with value: 0.8048207782525437 and parameters: {'n_estimators': 289, 'max_depth': 6, 'min_samples_split': 9, 'min_samples_leaf': 18, 'subsample': 0.924838903450534, 'learning_rate': 0.026744792627912416}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:21:58,328] Trial 32 finished with value: 0.8026778252926432 and parameters: {'n_estimators': 251, 'max_depth': 6, 'min_samples_split': 10, 'min_samples_leaf': 17, 'subsample': 0.9923561775816016, 'learning_rate': 0.013921527832226797}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:22:07,344] Trial 33 finished with value: 0.8058923025726726 and parameters: {'n_estimators': 278, 'max_depth': 5, 'min_samples_split': 8, 'min_samples_leaf': 19, 'subsample': 0.7138987414856087, 'learning_rate': 0.06428002551009815}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:22:22,581] Trial 34 finished with value: 0.8007136032399664 and parameters: {'n_estimators': 218, 'max_depth': 8, 'min_samples_split': 8, 'min_samples_leaf': 1, 'subsample': 0.8749958486712494, 'learning_rate': 0.046455084608237815}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:22:36,999] Trial 35 finished with value: 0.7985708416407803 and parameters: {'n_estimators': 300, 'max_depth': 7, 'min_samples_split': 6, 'min_samples_leaf': 15, 'subsample': 0.7719067704375175, 'learning_rate': 0.19105610280134194}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:22:44,327] Trial 36 finished with value: 0.7960704268664319 and parameters: {'n_estimators': 144, 'max_depth': 6, 'min_samples_split': 16, 'min_samples_leaf': 13, 'subsample': 0.9306235017391844, 'learning_rate': 0.010764647108201627}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:22:57,694] Trial 37 finished with value: 0.804643195509606 and parameters: {'n_estimators': 283, 'max_depth': 8, 'min_samples_split': 10, 'min_samples_leaf': 11, 'subsample': 0.6012167253972049, 'learning_rate': 0.04226112735004251}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:23:15,787] Trial 38 finished with value: 0.8019635714262477 and parameters: {'n_estimators': 271, 'max_depth': 10, 'min_samples_split': 3, 'min_samples_leaf': 19, 'subsample': 0.8304938999646336, 'learning_rate': 0.09473883420841474}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:23:23,005] Trial 39 finished with value: 0.8033912180358239 and parameters: {'n_estimators': 195, 'max_depth': 5, 'min_samples_split': 6, 'min_samples_leaf': 4, 'subsample': 0.7971591118819605, 'learning_rate': 0.07066815104902541}. Best is trial 13 with value: 0.8092855107599259.\n",
      "[I 2024-09-25 05:23:36,156] Trial 40 finished with value: 0.809642111451159 and parameters: {'n_estimators': 221, 'max_depth': 7, 'min_samples_split': 12, 'min_samples_leaf': 20, 'subsample': 0.9984062005148593, 'learning_rate': 0.02323859311247796}. Best is trial 40 with value: 0.809642111451159.\n",
      "[I 2024-09-25 05:23:48,833] Trial 41 finished with value: 0.8082136037183684 and parameters: {'n_estimators': 225, 'max_depth': 7, 'min_samples_split': 12, 'min_samples_leaf': 20, 'subsample': 0.9533577794258085, 'learning_rate': 0.024573791474791124}. Best is trial 40 with value: 0.809642111451159.\n",
      "[I 2024-09-25 05:24:01,468] Trial 42 finished with value: 0.809106301450916 and parameters: {'n_estimators': 225, 'max_depth': 7, 'min_samples_split': 12, 'min_samples_leaf': 20, 'subsample': 0.9313648333356307, 'learning_rate': 0.022936121263536752}. Best is trial 40 with value: 0.809642111451159.\n",
      "[I 2024-09-25 05:24:14,044] Trial 43 finished with value: 0.8074994455323301 and parameters: {'n_estimators': 222, 'max_depth': 7, 'min_samples_split': 13, 'min_samples_leaf': 20, 'subsample': 0.9450389334681218, 'learning_rate': 0.056762363125771764}. Best is trial 40 with value: 0.809642111451159.\n",
      "[I 2024-09-25 05:24:25,950] Trial 44 finished with value: 0.80696353985173 and parameters: {'n_estimators': 218, 'max_depth': 7, 'min_samples_split': 13, 'min_samples_leaf': 20, 'subsample': 0.943922449108692, 'learning_rate': 0.053890665851043004}. Best is trial 40 with value: 0.809642111451159.\n",
      "[I 2024-09-25 05:24:38,890] Trial 45 finished with value: 0.8098206509976685 and parameters: {'n_estimators': 227, 'max_depth': 7, 'min_samples_split': 15, 'min_samples_leaf': 20, 'subsample': 0.9706892594255614, 'learning_rate': 0.021824246512621475}. Best is trial 45 with value: 0.8098206509976685.\n",
      "[I 2024-09-25 05:24:49,498] Trial 46 finished with value: 0.8071428448410969 and parameters: {'n_estimators': 190, 'max_depth': 7, 'min_samples_split': 15, 'min_samples_leaf': 19, 'subsample': 0.9955592389966234, 'learning_rate': 0.07909102118394795}. Best is trial 45 with value: 0.8098206509976685.\n",
      "[I 2024-09-25 05:25:00,002] Trial 47 finished with value: 0.81053538326585 and parameters: {'n_estimators': 175, 'max_depth': 8, 'min_samples_split': 18, 'min_samples_leaf': 20, 'subsample': 0.8985629586173849, 'learning_rate': 0.023947757198782626}. Best is trial 47 with value: 0.81053538326585.\n",
      "[I 2024-09-25 05:25:08,716] Trial 48 finished with value: 0.7978563964136706 and parameters: {'n_estimators': 147, 'max_depth': 8, 'min_samples_split': 19, 'min_samples_leaf': 19, 'subsample': 0.8943192522755496, 'learning_rate': 0.2709183189187952}. Best is trial 47 with value: 0.81053538326585.\n",
      "[I 2024-09-25 05:25:12,664] Trial 49 finished with value: 0.7810725308774865 and parameters: {'n_estimators': 172, 'max_depth': 9, 'min_samples_split': 17, 'min_samples_leaf': 18, 'subsample': 0.2671786283641707, 'learning_rate': 0.20739145626157576}. Best is trial 47 with value: 0.81053538326585.\n",
      "[I 2024-09-25 05:25:23,844] Trial 50 finished with value: 0.8110708105446642 and parameters: {'n_estimators': 179, 'max_depth': 8, 'min_samples_split': 17, 'min_samples_leaf': 20, 'subsample': 0.9686376214087349, 'learning_rate': 0.03901742591038757}. Best is trial 50 with value: 0.8110708105446642.\n",
      "[I 2024-09-25 05:25:33,693] Trial 51 finished with value: 0.8089276662240493 and parameters: {'n_estimators': 157, 'max_depth': 8, 'min_samples_split': 17, 'min_samples_leaf': 20, 'subsample': 0.9677496870388604, 'learning_rate': 0.039537198064713404}. Best is trial 50 with value: 0.8110708105446642.\n",
      "[I 2024-09-25 05:25:44,471] Trial 52 finished with value: 0.8080350641718587 and parameters: {'n_estimators': 179, 'max_depth': 8, 'min_samples_split': 18, 'min_samples_leaf': 20, 'subsample': 0.8949379542280155, 'learning_rate': 0.02630140022071229}. Best is trial 50 with value: 0.8110708105446642.\n",
      "[I 2024-09-25 05:25:57,122] Trial 53 finished with value: 0.8117847773699881 and parameters: {'n_estimators': 198, 'max_depth': 8, 'min_samples_split': 15, 'min_samples_leaf': 18, 'subsample': 0.9571866488566668, 'learning_rate': 0.022995748583395814}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:26:05,846] Trial 54 finished with value: 0.8105343307819209 and parameters: {'n_estimators': 119, 'max_depth': 9, 'min_samples_split': 16, 'min_samples_leaf': 17, 'subsample': 0.9922246315332651, 'learning_rate': 0.041913589518873036}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:26:14,623] Trial 55 finished with value: 0.809106301450916 and parameters: {'n_estimators': 121, 'max_depth': 9, 'min_samples_split': 16, 'min_samples_leaf': 18, 'subsample': 0.9943480026412646, 'learning_rate': 0.04780578739292418}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:26:21,074] Trial 56 finished with value: 0.8082138907594398 and parameters: {'n_estimators': 81, 'max_depth': 10, 'min_samples_split': 15, 'min_samples_leaf': 17, 'subsample': 0.9627456490811642, 'learning_rate': 0.03818980829475223}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:26:33,945] Trial 57 finished with value: 0.8037491582520576 and parameters: {'n_estimators': 202, 'max_depth': 9, 'min_samples_split': 18, 'min_samples_leaf': 19, 'subsample': 0.8907569611068462, 'learning_rate': 0.10172541956732845}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:26:35,659] Trial 58 finished with value: 0.8042857336951581 and parameters: {'n_estimators': 99, 'max_depth': 9, 'min_samples_split': 16, 'min_samples_leaf': 19, 'subsample': 0.1881199663757645, 'learning_rate': 0.07528944377942284}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:26:43,973] Trial 59 finished with value: 0.8078562375842777 and parameters: {'n_estimators': 129, 'max_depth': 8, 'min_samples_split': 14, 'min_samples_leaf': 18, 'subsample': 0.9592603838353149, 'learning_rate': 0.060372543970269345}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:26:49,446] Trial 60 finished with value: 0.8030361482303056 and parameters: {'n_estimators': 189, 'max_depth': 8, 'min_samples_split': 19, 'min_samples_leaf': 17, 'subsample': 0.3468088324440268, 'learning_rate': 0.020528400926825403}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:27:06,316] Trial 61 finished with value: 0.8055361802832254 and parameters: {'n_estimators': 212, 'max_depth': 9, 'min_samples_split': 14, 'min_samples_leaf': 16, 'subsample': 0.9988955175745202, 'learning_rate': 0.017941649815174966}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:27:18,333] Trial 62 finished with value: 0.8112497328126026 and parameters: {'n_estimators': 170, 'max_depth': 8, 'min_samples_split': 15, 'min_samples_leaf': 18, 'subsample': 0.8522111434790901, 'learning_rate': 0.04070638911970218}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:27:29,440] Trial 63 finished with value: 0.8060708421191821 and parameters: {'n_estimators': 170, 'max_depth': 8, 'min_samples_split': 15, 'min_samples_leaf': 18, 'subsample': 0.8451874915142225, 'learning_rate': 0.04779850686856478}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:27:39,244] Trial 64 finished with value: 0.8044636034791676 and parameters: {'n_estimators': 154, 'max_depth': 8, 'min_samples_split': 17, 'min_samples_leaf': 19, 'subsample': 0.9156853852617581, 'learning_rate': 0.03775809227428752}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:27:51,342] Trial 65 finished with value: 0.8050004659633395 and parameters: {'n_estimators': 198, 'max_depth': 9, 'min_samples_split': 19, 'min_samples_leaf': 20, 'subsample': 0.814694800721693, 'learning_rate': 0.054789434568350844}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:28:05,282] Trial 66 finished with value: 0.8035714798287628 and parameters: {'n_estimators': 179, 'max_depth': 10, 'min_samples_split': 18, 'min_samples_leaf': 17, 'subsample': 0.964108929764686, 'learning_rate': 0.13268230883012447}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:28:18,506] Trial 67 finished with value: 0.8096424941725878 and parameters: {'n_estimators': 241, 'max_depth': 7, 'min_samples_split': 16, 'min_samples_leaf': 18, 'subsample': 0.9115302564367663, 'learning_rate': 0.03184048442156788}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:28:33,162] Trial 68 finished with value: 0.8057138587065201 and parameters: {'n_estimators': 238, 'max_depth': 8, 'min_samples_split': 16, 'min_samples_leaf': 15, 'subsample': 0.8601849410803665, 'learning_rate': 0.03328356666746967}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:28:46,587] Trial 69 finished with value: 0.8057141457475918 and parameters: {'n_estimators': 248, 'max_depth': 7, 'min_samples_split': 20, 'min_samples_leaf': 18, 'subsample': 0.9062002237798772, 'learning_rate': 0.04772459221762469}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:29:02,173] Trial 70 finished with value: 0.8030348087053051 and parameters: {'n_estimators': 231, 'max_depth': 9, 'min_samples_split': 17, 'min_samples_leaf': 16, 'subsample': 0.8797149799200948, 'learning_rate': 0.08681670108460132}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:29:14,522] Trial 71 finished with value: 0.8076776980377681 and parameters: {'n_estimators': 212, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 19, 'subsample': 0.9737873486697419, 'learning_rate': 0.02508999479776237}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:29:24,822] Trial 72 finished with value: 0.8028567475605813 and parameters: {'n_estimators': 210, 'max_depth': 6, 'min_samples_split': 16, 'min_samples_leaf': 20, 'subsample': 0.9281592981703434, 'learning_rate': 0.01846978612250736}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:29:35,492] Trial 73 finished with value: 0.8064281125729157 and parameters: {'n_estimators': 186, 'max_depth': 7, 'min_samples_split': 15, 'min_samples_leaf': 18, 'subsample': 0.9417729390242499, 'learning_rate': 0.03289011323976925}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:29:39,881] Trial 74 finished with value: 0.808392238945235 and parameters: {'n_estimators': 62, 'max_depth': 8, 'min_samples_split': 17, 'min_samples_leaf': 19, 'subsample': 0.9784482016931757, 'learning_rate': 0.04013827587104357}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:29:46,067] Trial 75 finished with value: 0.8033924618804673 and parameters: {'n_estimators': 161, 'max_depth': 7, 'min_samples_split': 15, 'min_samples_leaf': 8, 'subsample': 0.49718901937397936, 'learning_rate': 0.06785768293407138}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:29:58,652] Trial 76 finished with value: 0.8071422707589537 and parameters: {'n_estimators': 198, 'max_depth': 8, 'min_samples_split': 18, 'min_samples_leaf': 17, 'subsample': 0.8464431316580653, 'learning_rate': 0.010380068047948682}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:30:06,312] Trial 77 finished with value: 0.8032141136946721 and parameters: {'n_estimators': 138, 'max_depth': 6, 'min_samples_split': 16, 'min_samples_leaf': 20, 'subsample': 0.9081034917597675, 'learning_rate': 0.019647949071501254}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:30:21,203] Trial 78 finished with value: 0.7962497318557991 and parameters: {'n_estimators': 231, 'max_depth': 8, 'min_samples_split': 13, 'min_samples_leaf': 19, 'subsample': 0.8657428195251403, 'learning_rate': 0.23794560672160708}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:30:30,194] Trial 79 finished with value: 0.80696353985173 and parameters: {'n_estimators': 167, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 18, 'subsample': 0.7957150525572418, 'learning_rate': 0.031500328380557906}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:30:46,282] Trial 80 finished with value: 0.8064276341711297 and parameters: {'n_estimators': 245, 'max_depth': 8, 'min_samples_split': 17, 'min_samples_leaf': 20, 'subsample': 0.9424369877870429, 'learning_rate': 0.05849235457168471}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:30:56,867] Trial 81 finished with value: 0.8069637312124444 and parameters: {'n_estimators': 176, 'max_depth': 7, 'min_samples_split': 15, 'min_samples_leaf': 17, 'subsample': 0.9748150560921622, 'learning_rate': 0.015298568083309282}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:31:09,997] Trial 82 finished with value: 0.8073210973465349 and parameters: {'n_estimators': 194, 'max_depth': 8, 'min_samples_split': 16, 'min_samples_leaf': 16, 'subsample': 0.9165210149193483, 'learning_rate': 0.02719419478229987}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:31:22,076] Trial 83 finished with value: 0.8069643052945875 and parameters: {'n_estimators': 254, 'max_depth': 7, 'min_samples_split': 18, 'min_samples_leaf': 15, 'subsample': 0.7288640758612116, 'learning_rate': 0.04493060088455459}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:31:33,516] Trial 84 finished with value: 0.8055346493975103 and parameters: {'n_estimators': 185, 'max_depth': 8, 'min_samples_split': 12, 'min_samples_leaf': 19, 'subsample': 0.8219857286340829, 'learning_rate': 0.010193210043358224}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:31:41,279] Trial 85 finished with value: 0.8085707784917447 and parameters: {'n_estimators': 111, 'max_depth': 9, 'min_samples_split': 14, 'min_samples_leaf': 17, 'subsample': 0.8831744779122155, 'learning_rate': 0.028402806005764645}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:31:55,797] Trial 86 finished with value: 0.8083918562238063 and parameters: {'n_estimators': 263, 'max_depth': 8, 'min_samples_split': 13, 'min_samples_leaf': 18, 'subsample': 0.7699585711261594, 'learning_rate': 0.01907552434352595}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:32:08,634] Trial 87 finished with value: 0.8032130612107431 and parameters: {'n_estimators': 208, 'max_depth': 7, 'min_samples_split': 11, 'min_samples_leaf': 5, 'subsample': 0.9537277759996595, 'learning_rate': 0.17431921495513042}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:32:24,051] Trial 88 finished with value: 0.8073209059858205 and parameters: {'n_estimators': 218, 'max_depth': 9, 'min_samples_split': 19, 'min_samples_leaf': 20, 'subsample': 0.9789853810514566, 'learning_rate': 0.03819231797234598}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:32:33,781] Trial 89 finished with value: 0.8078575771092783 and parameters: {'n_estimators': 204, 'max_depth': 6, 'min_samples_split': 16, 'min_samples_leaf': 19, 'subsample': 0.9347404473481953, 'learning_rate': 0.04268117414553029}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:32:41,026] Trial 90 finished with value: 0.8069642096142302 and parameters: {'n_estimators': 149, 'max_depth': 8, 'min_samples_split': 15, 'min_samples_leaf': 18, 'subsample': 0.6826541227645284, 'learning_rate': 0.05017095369297196}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:32:52,145] Trial 91 finished with value: 0.8085707784917445 and parameters: {'n_estimators': 183, 'max_depth': 7, 'min_samples_split': 12, 'min_samples_leaf': 20, 'subsample': 0.9981737415850119, 'learning_rate': 0.022104429769018205}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:33:04,940] Trial 92 finished with value: 0.807856428944992 and parameters: {'n_estimators': 229, 'max_depth': 7, 'min_samples_split': 10, 'min_samples_leaf': 19, 'subsample': 0.9334455947057793, 'learning_rate': 0.03206539348491681}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:33:17,821] Trial 93 finished with value: 0.81053538326585 and parameters: {'n_estimators': 223, 'max_depth': 7, 'min_samples_split': 12, 'min_samples_leaf': 20, 'subsample': 0.9001332195689876, 'learning_rate': 0.022102365820157628}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:33:29,415] Trial 94 finished with value: 0.8023207461996241 and parameters: {'n_estimators': 241, 'max_depth': 6, 'min_samples_split': 17, 'min_samples_leaf': 19, 'subsample': 0.9047707777705, 'learning_rate': 0.016541679792164525}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:33:41,872] Trial 95 finished with value: 0.8060715118816825 and parameters: {'n_estimators': 214, 'max_depth': 8, 'min_samples_split': 7, 'min_samples_leaf': 20, 'subsample': 0.8482927563341802, 'learning_rate': 0.02578623086208981}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:33:53,889] Trial 96 finished with value: 0.8091072582544879 and parameters: {'n_estimators': 225, 'max_depth': 7, 'min_samples_split': 13, 'min_samples_leaf': 18, 'subsample': 0.8797414022001644, 'learning_rate': 0.03635178083905323}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:34:03,451] Trial 97 finished with value: 0.803749828014558 and parameters: {'n_estimators': 237, 'max_depth': 7, 'min_samples_split': 14, 'min_samples_leaf': 11, 'subsample': 0.58777123843455, 'learning_rate': 0.06300754699556524}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:34:14,038] Trial 98 finished with value: 0.8062492859853344 and parameters: {'n_estimators': 161, 'max_depth': 8, 'min_samples_split': 11, 'min_samples_leaf': 13, 'subsample': 0.9578165351036557, 'learning_rate': 0.05046391131673638}. Best is trial 53 with value: 0.8117847773699881.\n",
      "[I 2024-09-25 05:34:25,674] Trial 99 finished with value: 0.7989281120945139 and parameters: {'n_estimators': 174, 'max_depth': 9, 'min_samples_split': 17, 'min_samples_leaf': 17, 'subsample': 0.9163849322646299, 'learning_rate': 0.22291964121219418}. Best is trial 53 with value: 0.8117847773699881.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejores hiperparámetros: {'n_estimators': 198, 'max_depth': 8, 'min_samples_split': 15, 'min_samples_leaf': 18, 'subsample': 0.9571866488566668, 'learning_rate': 0.022995748583395814}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.022995748583395814, max_depth=8,\n",
       "                           min_samples_leaf=18, min_samples_split=15,\n",
       "                           n_estimators=198, random_state=42,\n",
       "                           subsample=0.9571866488566668)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(learning_rate=0.022995748583395814, max_depth=8,\n",
       "                           min_samples_leaf=18, min_samples_split=15,\n",
       "                           n_estimators=198, random_state=42,\n",
       "                           subsample=0.9571866488566668)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.022995748583395814, max_depth=8,\n",
       "                           min_samples_leaf=18, min_samples_split=15,\n",
       "                           n_estimators=198, random_state=42,\n",
       "                           subsample=0.9571866488566668)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def objective(trial):\n",
    "    # Definir los hiperparámetros que queremos optimizar\n",
    "    n_estimators = trial.suggest_int('n_estimators', 50, 300)\n",
    "    max_depth = trial.suggest_int('max_depth', 3, 10)\n",
    "    min_samples_split = trial.suggest_int('min_samples_split', 2, 20)\n",
    "    min_samples_leaf = trial.suggest_int('min_samples_leaf', 1, 20)\n",
    "    subsample = trial.suggest_float('subsample', 0.1, 1.0)\n",
    "    learning_rate = trial.suggest_float('learning_rate', 0.01, 0.3)\n",
    "    \n",
    "    # Crear el modelo con los hiperparámetros actuales\n",
    "    model = GradientBoostingClassifier(\n",
    "        n_estimators=n_estimators,\n",
    "        max_depth=max_depth,\n",
    "        min_samples_split=min_samples_split,\n",
    "        min_samples_leaf=min_samples_leaf,\n",
    "        subsample=subsample,\n",
    "        learning_rate=learning_rate,\n",
    "        random_state=42\n",
    "    )\n",
    "    \n",
    "    # Entrenar y evaluar el modelo usando validación cruzada\n",
    "    cv_scores = cross_val_score(model, X_train, y_train, cv=3, scoring='accuracy')\n",
    "    \n",
    "    # Retornar la media de los puntajes de validación (queremos maximizar el accuracy)\n",
    "    return cv_scores.mean()\n",
    "\n",
    "# Crear el estudio de Optuna para encontrar los mejores hiperparámetros\n",
    "study = optuna.create_study(direction='maximize')\n",
    "study.optimize(objective, n_trials=100)\n",
    "\n",
    "# Mostrar los mejores hiperparámetros\n",
    "best_params = study.best_params\n",
    "print(\"Mejores hiperparámetros:\", best_params)\n",
    "\n",
    "# Entrenar el modelo final con los mejores hiperparámetros\n",
    "final_model = GradientBoostingClassifier(\n",
    "    **best_params,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "final_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Puntajes de validación cruzada (K-Fold): [0.53778888 0.70875    0.766875   0.794375   0.538125  ]\n",
      "Media de los puntajes de validación cruzada: 0.6691827763897564\n",
      "Precisión en el conjunto de prueba: 0.7913369429404414\n"
     ]
    }
   ],
   "source": [
    "y_pred = final_model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "k_folds = 5\n",
    "\n",
    "# Realizar la validación cruzada\n",
    "cv_scores = cross_val_score(final_model, X, y, cv=k_folds, scoring='accuracy')\n",
    "\n",
    "# Mostrar los puntajes obtenidos en cada fold\n",
    "print(f\"Puntajes de validación cruzada (K-Fold): {cv_scores}\")\n",
    "print(f\"Media de los puntajes de validación cruzada: {cv_scores.mean()}\")\n",
    "print(f\"Precisión en el conjunto de prueba: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión del modelo: 0.7913369429404414\n",
      "Reporte de clasificación:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       False       0.82      0.77      0.79      1183\n",
      "        True       0.79      0.83      0.81      1218\n",
      "\n",
      "    accuracy                           0.80      2401\n",
      "   macro avg       0.80      0.80      0.80      2401\n",
      "weighted avg       0.80      0.80      0.80      2401\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizar la matriz de confusión\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Clase 0\", \"Clase 1\"], yticklabels=[\"Clase 0\", \"Clase 1\"])\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "print(f\"Precisión del modelo: {accuracy}\")\n",
    "print(\"Reporte de clasificación:\\n\", class_report)\n",
    "\n",
    "# Calcular y graficar la curva ROC\n",
    "y_prob = final_model.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "feature_importance = final_model.feature_importances_\n",
    "\n",
    "# Crear un DataFrame para visualizarlo mejor (asumiendo que tienes los nombres de las características)\n",
    "feature_names = X.columns if isinstance(X, pd.DataFrame) else [f'Feature {i}' for i in range(X.shape[1])]\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Importance': feature_importance\n",
    "})\n",
    "\n",
    "# Ordenar de mayor a menor importancia\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Visualizar la importancia de las características\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'], color='skyblue')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Feature Importances in GradientBoostingClassifier')\n",
    "plt.gca().invert_yaxis()  # Para que las características más importantes estén en la parte superior\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alberto\\AppData\\Local\\Temp\\ipykernel_19144\\2745101116.py:8: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[1 0 1 ... 1 0 1]' has dtype incompatible with bool, please explicitly cast to a compatible dtype first.\n",
      "  df_test.loc[:, 'CryoSleep'] = df_test['CryoSleep'].astype(int)\n"
     ]
    }
   ],
   "source": [
    "df_t = pd.read_csv('Test_recoded.csv')\n",
    "\n",
    "df_test = df_t.drop(columns = ['Transported'] + columnas_no_usadas + features_eliminadas)\n",
    "\n",
    "df_test.loc[:, 'HomePlanet'] = df_test['HomePlanet'].map({'Europa': 0, 'Earth': 1, 'Mars': 2}).astype(int)\n",
    "df_test.loc[:, 'CabinDeck'] = df_test['CabinDeck'].map({'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'T': 7}).astype(int)\n",
    "df_test.loc[:, 'CabinSide'] = df_test['CabinSide'].map({'P': 0, 'S': 1}).astype(int)\n",
    "df_test.loc[:, 'CryoSleep'] = df_test['CryoSleep'].astype(int)\n",
    "df_test.loc[:, 'Destination'] = df_test['Destination'].map({'TRAPPIST-1e': 0, 'PSO J318.5-22': 1, '55 Cancri e': 2}).astype(int)\n",
    "\n",
    "df_test = df_test.astype(int)\n",
    "\n",
    "prediccion = final_model_2.predict(df_test)\n",
    "\n",
    "df_predict = pd.DataFrame({\n",
    "    'PassengerId': df_t['PassengerId'],\n",
    "    'Transported': prediccion.astype(bool)\n",
    "})\n",
    "\n",
    "df_predict.to_csv('prediccion_gb_optuna.csv', index = False)"
   ]
  }
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